RDF Semantic Graph Overview

1.1 Introduction to Oracle Semantic Technologies Support

Oracle Database enables you to store semantic data and ontologies, to query semantic data and to perform ontology-assisted query of enterprise relational data, and to use supplied or user-defined inferencing to expand the power of querying on semantic data.

Figure 1-1 shows how these capabilities interact.

Figure 1-1 Oracle Semantic Capabilities

Description of "Figure 1-1 Oracle Semantic Capabilities"

As shown in Figure 1-1, the database contains semantic data and ontologies (RDF/OWL models), as well as traditional relational data. To load semantic data, bulk loading is the most efficient approach, although you can load data incrementally using transactional INSERT statements.

Note:

If you want to use existing semantic data from a release before Oracle Database 11.1, the data must be upgraded as described in Enabling RDF Semantic Graph Support.

You can query semantic data and ontologies, and you can also perform ontology-assisted queries of semantic and traditional relational data to find semantic relationships. To perform ontology-assisted queries, use the SEM_RELATED operator, which is described in Using Semantic Operators to Query Relational Data.

You can expand the power of queries on semantic data by using inferencing, which uses rules in rulebases. Inferencing enables you to make logical deductions based on the data and the rules. For information about using rules and rulebases for inferencing, see Inferencing: Rules and Rulebases.

Parent topic: RDF Semantic Graph Overview

1.2 Semantic Data Modeling

In addition to its formal semantics, semantic data has a simple data structure that is effectively modeled using a directed graph.

The metadata statements are represented as triples: nodes are used to represent two parts of the triple, and the third part is represented by a directed link that describes the relationship between the nodes. The triples are stored in a semantic data network. In addition, information is maintained about specific semantic data models created by database users. A user-created model has a model name, and refers to triples stored in a specified table column.

Statements are expressed in triples: {subject or resource, predicate or property, object or value}. In this manual, {subject, property, object} is used to describe a triple, and the terms statement and triple may sometimes be used interchangeably. Each triple is a complete and unique fact about a specific domain, and can be represented by a link in a directed graph.

Parent topic: RDF Semantic Graph Overview

1.3 Semantic Data in the Database

There is one universe for all semantic data stored in the database.

All triples are parsed and stored in the system as entries in tables under the MDSYS schema. A triple {subject, property, object} is treated as one database object. As a result, a single document containing multiple triples results in multiple database objects.

All the subjects and objects of triples are mapped to nodes in a semantic data network, and properties are mapped to network links that have their start node and end node as subject and object, respectively. The possible node types are blank nodes, URIs, plain literals, and typed literals.

The following requirements apply to the specifications of URIs and the storage of semantic data in the database:

A subject must be a URI or a blank node.
A property must be a URI.
An object can be any type, such as a URI, a blank node, or a literal. (However, null values and null strings are not supported.)

Parent topic: RDF Semantic Graph Overview

1.3.1 Metadata for Models

The MDSYS.SEM_MODEL$ view contains information about all models defined in the database. When you create a model using the SEM_APIS.CREATE_SEM_MODEL procedure, you specify a name for the model, as well as a table and column to hold references to the semantic data, and the system automatically generates a model ID.

Oracle maintains the MDSYS.SEM_MODEL$ view automatically when you create and drop models. Users should never modify this view directly. For example, do not use SQL INSERT, UPDATE, or DELETE statements with this view.

The MDSYS.SEM_MODEL$ view contains the columns shown in Table 1-1.

Table 1-1 MDSYS.SEM_MODEL$ View Columns

Column Name	Data Type	Description
OWNER	VARCHAR2(30)	Schema of the owner of the model.
MODEL_ID	NUMBER	Unique model ID number, automatically generated.
MODEL_NAME	VARCHAR2(25)	Name of the model.
TABLE_NAME	VARCHAR2(30)	Name of the table to hold references to semantic data for the model.
COLUMN_NAME	VARCHAR2(30)	Name of the column of type SDO_RDF_TRIPLE_S in the table to hold references to semantic data for the model.
MODEL_TABLESPACE_NAME	VARCHAR2(30)	Name of the tablespace to be used for storing the triples for this model.
MODEL_TYPE	VARCHAR2(40)	A value indicating the type of RDF model: `M` for regular model; `V` for virtual model; `X` for model created to store the contents of the semantic index; or `D` for model created on relational data.

When you create a model, a view for the triples associated with the model is also created under the MDSYS schema. This view has a name in the format SEMM_model-name, and it is visible only to the owner of the model and to users with suitable privileges. Each MDSYS.SEMM_model-name view contains a row for each triple (stored as a link in a network), and it has the columns shown in Table 1-2.

Table 1-2 MDSYS.SEMM_model-name View Columns

Column Name	Data Type	Description
P_VALUE_ID	NUMBER	The VALUE_ID for the text value of the predicate of the triple. Part of the primary key.
START_NODE_ID	NUMBER	The VALUE_ID for the text value of the subject of the triple. Also part of the primary key.
CANON_END_NODE_ID	NUMBER	The VALUE_ID for the text value of the canonical form of the object of the triple. Also part of the primary key.
END_NODE_ID	NUMBER	The VALUE_ID for the text value of the object of the triple
MODEL_ID	NUMBER	The ID for the RDF model to which the triple belongs.
COST	NUMBER	(Reserved for future use)
CTXT1	NUMBER	(Reserved column; can be used for fine-grained access control)
CTXT2	VARCHAR2(4000)	(Reserved for future use)
DISTANCE	NUMBER	(Reserved for future use)
EXPLAIN	VARCHAR2(4000)	(Reserved for future use)
PATH	VARCHAR2(4000)	(Reserved for future use)
G_ID	NUMBER	The VALUE_ID for the text value of the graph name for the triple. Null indicates the default graph (see Named Graphs).
LINK_ID	VARCHAR2(71)	Unique triple identifier value. (It is currently a computed column, and its definition may change in a future release.)

Note:

In Table 1-2, for columns P_VALUE_ID, START_NODE_ID, END_NODE_ID, CANON_END_NODE_ID, and G_ID, the actual ID values are computed from the corresponding lexical values. However, a lexical value may not always map to the same ID value.

Parent topic: Semantic Data in the Database

1.3.2 Statements

The MDSYS.RDF_VALUE$ table contains information about the subjects, properties, and objects used to represent RDF statements. It uniquely stores the text values (URIs or literals) for these three pieces of information, using a separate row for each part of each triple.

Oracle maintains the MDSYS.RDF_VALUE$ table automatically. Users should never modify this view directly. For example, do not use SQL INSERT, UPDATE, or DELETE statements with this view.

The RDF_VALUE$ table contains the columns shown in Table 1-3.

Table 1-3 MDSYS.RDF_VALUE$ Table Columns

Column Name	Data Type	Description
VALUE_ID	NUMBER	Unique value ID number, automatically generated.
VALUE_TYPE	VARCHAR2(10)	The type of text information stored in the VALUE_NAME column. Possible values: `UR` for URI, `BN` for blank node, `PL` for plain literal, `PL@` for plain literal with a language tag, `PLL` for plain long literal, `PLL@` for plain long literal with a language tag, `TL` for typed literal, or `TLL` for typed long literal. A long literal is a literal with more than 4000 bytes.
VNAME_PREFIX	VARCHAR2(4000)	If the length of the lexical value is 4000 bytes or less, this column stores a prefix of a portion of the lexical value. The SEM_APIS.VALUE_NAME_PREFIX function can be used for prefix computation. For example, the prefix for the portion of the lexical value `<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>` without the angle brackets is `http://www.w3.org/1999/02/22-rdf-syntax-ns#`.
VNAME_SUFFIX	VARCHAR2(512)	If the length of the lexical value is 4000 bytes or less, this column stores a suffix of a portion of the lexical value. The SEM_APIS.VALUE_NAME_SUFFIX function can be used for suffix computation. For the lexical value mentioned in the description of the VNAME_PREFIX column, the suffix is `type`.
LITERAL_TYPE	VARCHAR2(4000)	For typed literals, the type information; otherwise, null. For example, for a row representing a creation date of 1999-08-16, the VALUE_TYPE column can contain `TL`, and the LITERAL_TYPE column can contain `http://www.w3.org/2001/XMLSchema#date`.
LANGUAGE_TYPE	VARCHAR2(80)	Language tag (for example, `fr` for French) for a literal with a language tag (that is, if VALUE_TYPE is `PL@` or `PLL@`). Otherwise, this column has a null value.
CANON_ID	NUMBER	The ID for the canonical lexical value for the current lexical value. (The use of this column may change in a future release.)
COLLISION_EXT	VARCHAR2(64)	Used for collision handling for the lexical value. (The use of this column may change in a future release.)
CANON_COLLISION_EXT	VARCHAR2(64)	Used for collision handling for the canonical lexical value. (The use of this column may change in a future release.)
ORDER_TYPE	NUMBER	Represents order based on data type. Used to improve performance on ORDER BY queries.
ORDER_NUM	NUMBER	Represents order for number type. Used to improve performance on ORDER BY queries.
ORDER_DATE	TIMESTAMP WITH TIME ZONE	Represents order based on date type Used to improve performance on ORDER BY queries.
LONG_VALUE	CLOB	The character string if the length of the lexical value is greater than 4000 bytes. Otherwise, this column has a null value.
GEOM	SDO_GEOMETRY	A geometry value when a spatial index is defined.
VALUE_NAME	VARCHAR2(4000)	This is a computed column. If length of the lexical value is 4000 bytes or less, the value of this column is the concatenation of the values of VNAME_PREFIX column and the VNAME_SUFFIX column.

Triple Uniqueness and Data Types for Literals

Parent topic: Semantic Data in the Database

1.3.2.1 Triple Uniqueness and Data Types for Literals

Duplicate triples are not stored in the database. To check if a triple is a duplicate of an existing triple, the subject, property, and object of the incoming triple are checked against triple values in the specified model. If the incoming subject, property, and object are all URIs, an exact match of their values determines a duplicate. However, if the object of incoming triple is a literal, an exact match of the subject and property, and a value (canonical) match of the object, determine a duplicate. For example, the following two triples are duplicates:

<eg:a> <eg:b> <"123"^^http://www.w3.org/2001/XMLSchema#int>
<eg:a> <eg:b> <"123"^^http://www.w3.org/2001/XMLSchema#unsignedByte>

The second triple is treated as a duplicate of the first, because "123"^^<http://www.w3.org/2001/XMLSchema#int> has an equivalent value (is canonically equivalent) to "123"^^<http://www.w3.org/2001/XMLSchema#unsignedByte>. Two entities are canonically equivalent if they can be reduced to the same value.

To use a non-RDF example, A*(B-C), A*B-C*A, (B-C)*A, and -A*C+A*B all convert into the same canonical form.

Note:

Although duplicate triples and quads are not stored in the underlying table partition for the MDSYS.RDFM_<model> view, it is possible to have duplicate rows in an application table. For example, if a triple is inserted multiple times into an application table, it will appear once in the MDSYS.RDFM_<model> view, but will occupy multiple rows in the application table.

Value-based matching of lexical forms is supported for the following data types:

STRING: plain literal, xsd:string and some of its XML Schema subtypes
NUMERIC: xsd:decimal and its XML Schema subtypes, xsd:float, and xsd:double. (Support is not provided for float/double INF, -INF, and NaN values.)
DATETIME: xsd:datetime, with support for time zone. (Without time zone there are still multiple representations for a single value, for example, "2004-02-18T15:12:54" and "2004-02-18T15:12:54.0000".)
DATE: xsd:date, with or without time zone
OTHER: Everything else. (No attempt is made to match different representations).

Canonicalization is performed when the time zone is present for literals of type xsd:time and xsd:dateTime.

The following namespace definition is used: xmlns:xsd="http://www.w3.org/2001/XMLSchema"

The first occurrence of a literal in the RDF_VALUE$ table is taken as the canonical form and given the VALUE_TYPE value of CPL, CPL@, CTL, CPLL, CPLL@, or CTLL as appropriate; that is, a C for canonical is prefixed to the actual value type. If a literal with the same canonical form (but a different lexical representation) as a previously inserted literal is inserted into the RDF_VALUE$ table, the VALUE_TYPE value assigned to the new insert is PL, PL@, TL, PLL, PLL@, or TLL as appropriate.

Canonically equivalent text values having different lexical representations are thus stored in the RDF_VALUE$ table; however, canonically equivalent triples are not stored in the database.

Parent topic: Statements

1.3.3 Subjects and Objects

RDF subjects and objects are mapped to nodes in a semantic data network. Subject nodes are the start nodes of links, and object nodes are the end nodes of links. Non-literal nodes (that is, URIs and blank nodes) can be used as both subject and object nodes. Literals can be used only as object nodes.

Parent topic: Semantic Data in the Database

1.3.4 Blank Nodes

Blank nodes can be used as subject and object nodes in the semantic network. Blank node identifiers are different from URIs in that they are scoped within a semantic model. Thus, although multiple occurrences of the same blank node identifier within a single semantic model necessarily refer to the same resource, occurrences of the same blank node identifier in two different semantic models do not refer to the same resource.

In an Oracle semantic network, this behavior is modeled by requiring that blank nodes are always reused (that is, are used to represent the same resource if the same blank node identifier is used) within a semantic model, and never reused between two different models. Thus, when inserting triples involving blank nodes into a model, you must use the SDO_RDF_TRIPLE_S constructor that supports reuse of blank nodes.

Parent topic: Semantic Data in the Database

1.3.5 Properties

Properties are mapped to links that have their start node and end node as subjects and objects, respectively. Therefore, a link represents a complete triple.

When a triple is inserted into a model, the subject, property, and object text values are checked to see if they already exist in the database. If they already exist (due to previous statements in other models), no new entries are made; if they do not exist, three new rows are inserted into the RDF_VALUE$ table (described in Statements).

Parent topic: Semantic Data in the Database

1.3.6 Inferencing: Rules and Rulebases

Inferencing is the ability to make logical deductions based on rules. Inferencing enables you to construct queries that perform semantic matching based on meaningful relationships among pieces of data, as opposed to just syntactic matching based on string or other values. Inferencing involves the use of rules, either supplied by Oracle or user-defined, placed in rulebases.

Figure 1-2 shows triple sets being inferred from model data and the application of rules in one or more rulebases. In this illustration, the database can have any number of semantic models, rulebases, and inferred triple sets, and an inferred triple set can be derived using rules in one or more rulebases.

Figure 1-2 Inferencing

Description of "Figure 1-2 Inferencing"

A rule is an object that can be applied to draw inferences from semantic data. A rule is identified by a name and consists of:

An IF side pattern for the antecedents
An optional filter condition that further restricts the subgraphs matched by the IF side pattern
A THEN side pattern for the consequents

For example, the rule that a chairperson of a conference is also a reviewer of the conference could be represented as follows:

('chairpersonRule', -- rule name
 '(?r :ChairPersonOf ?c)', -- IF side pattern
 NULL,  -- filter condition
 '(?r :ReviewerOf ?c)', -- THEN side pattern
 SEM_ALIASES (SEM_ALIAS('', 'http://some.org/test/'))
)

In this case, the rule does not have a filter condition, so that component of the representation is NULL. For best performance, use a single-triple pattern on the THEN side of the rule. If a rule has multiple triple patterns on the THEN side, you can easily break it into multiple rules, each with a single-triple pattern, on the THEN side.

A rulebase is an object that contains rules. The following Oracle-supplied rulebases are provided:

RDFS
RDF (a subset of RDFS)
OWLSIF (empty)
RDFS++ (empty)
OWL2RL (empty)
OWLPrime (empty)
SKOSCORE (empty)

The RDFS and RDF rulebases are created when you call the SEM_APIS.CREATE_SEM_NETWORK procedure to add RDF support to the database. The RDFS rulebase implements the RDFS entailment rules, as described in the World Wide Web Consortium (W3C) RDF Semantics document at http://www.w3.org/TR/rdf-mt/. The RDF rulebase represents the RDF entailment rules, which are a subset of the RDFS entailment rules. You can see the contents of these rulebases by examining the MDSYS.SEMR_RDFS and MDSYS.SEMR_RDF views.

You can also create user-defined rulebases using the SEM_APIS.CREATE_RULEBASE procedure. User-defined rulebases enable you to provide additional specialized inferencing capabilities.

For each rulebase, a system table is created to hold rules in the rulebase, along with a system view with a name in the format MDSYS.SEMR_rulebase-name (for example, MDSYS.SEMR_FAMILY_RB for a rulebase named FAMILY_RB). You must use this view to insert, delete, and modify rules in the rulebase. Each MDSYS.SEMR_rulebase-name view has the columns shown in Table 1-4.

Table 1-4 MDSYS.SEMR_rulebase-name View Columns

Column Name	Data Type	Description
RULE_NAME	VARCHAR2(30)	Name of the rule
ANTECEDENTS	VARCHAR2(4000)	IF side pattern for the antecedents
FILTER	VARCHAR2(4000)	Filter condition that further restricts the subgraphs matched by the IF side pattern. Null indicates no filter condition is to be applied.
CONSEQUENTS	VARCHAR2(4000)	THEN side pattern for the consequents
ALIASES	SEM_ALIASES	One or more namespaces to be used. (The SEM_ALIASES data type is described in Using the SEM_MATCH Table Function to Query Semantic Data.)

Information about all rulebases is maintained in the MDSYS.SEM_RULEBASE_INFO view, which has the columns shown in Table 1-5 and one row for each rulebase.

Table 1-5 MDSYS.SEM_RULEBASE_INFO View Columns

Column Name	Data Type	Description
OWNER	VARCHAR2(30)	Owner of the rulebase
RULEBASE_NAME	VARCHAR2(25)	Name of the rulebase
RULEBASE_VIEW_NAME	VARCHAR2(30)	Name of the view that you must use for any SQL statements that insert, delete, or modify rules in the rulebase
STATUS	VARCHAR2(30)	Contains `VALID` if the rulebase is valid, `INPROGRESS` if the rulebase is being created, or `FAILED` if a system failure occurred during the creation of the rulebase.

Example 1-1 Inserting a Rule into a Rulebase

Example 1-1 creates a rulebase named family_rb, and then inserts a rule named grandparent_rule into the family_rb rulebase. This rule says that if a person is the parent of a child who is the parent of a child, that person is a grandparent of (that is, has the grandParentOf relationship with respect to) his or her child's child. It also specifies a namespace to be used. (This example is an excerpt from Example 1-110 in Example: Family Information.)

EXECUTE SEM_APIS.CREATE_RULEBASE('family_rb');

INSERT INTO mdsys.semr_family_rb VALUES(
  'grandparent_rule',
  '(?x :parentOf ?y) (?y :parentOf ?z)',
  NULL,
  '(?x :grandParentOf ?z)', 
  SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')));

Note that the kind of grandparent rule shown in Example 1-1 can be implemented using the OWL 2 property chain construct. For information about property chain handling, see Property Chain Handling.

Example 1-2 Using Rulebases for Inferencing

You can specify one or more rulebases when calling the SEM_MATCH table function (described in Using the SEM_MATCH Table Function to Query Semantic Data), to control the behavior of queries against semantic data. Example 1-2 refers to the family_rb rulebase and to the grandParentOf relationship created in Example 1-1, to find all grandfathers (grandparents who are male) and their grandchildren. (This example is an excerpt from Example 1-110 in Example: Family Information.)

-- Select all grandfathers and their grandchildren from the family model.
-- Use inferencing from both the RDFS and family_rb rulebases.
SELECT x grandfather, y grandchild
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male}',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

For information about support for native OWL inferencing, see Using OWL Inferencing.

Parent topic: Semantic Data in the Database

1.3.7 Entailments (Rules Indexes)

An entailment (rules index) is an object containing precomputed triples that can be inferred from applying a specified set of rulebases to a specified set of models. If a SEM_MATCH query refers to any rulebases, an entailment must exist for each rulebase-model combination in the query.

To create an entailment, use the SEM_APIS.CREATE_ENTAILMENT procedure. To drop (delete) an entailment, use the SEM_APIS.DROP_ENTAILMENT procedure.

When you create an entailment, a view for the triples associated with the entailment is also created under the MDSYS schema. This view has a name in the format SEMI_entailment-name, and it is visible only to the owner of the entailment and to users with suitable privileges. Each MDSYS.SEMI_entailment-name view contains a row for each triple (stored as a link in a network), and it has the same columns as the SEMM_model-name view, which is described in Table 1-2 in Metadata for Models.

Information about all entailments is maintained in the MDSYS.SEM_RULES_INDEX_INFO view, which has the columns shown in Table 1-6 and one row for each entailment.

Table 1-6 MDSYS.SEM_RULES_INDEX_INFO View Columns

Column Name	Data Type	Description
OWNER	VARCHAR2(30)	Owner of the entailment
INDEX_NAME	VARCHAR2(25)	Name of the entailment
INDEX_VIEW_NAME	VARCHAR2(30)	Name of the view that you must use for any SQL statements that insert, delete, or modify rules in the entailment
STATUS	VARCHAR2(30)	Contains `VALID` if the entailment is valid, `INVALID` if the entailment is not valid, `INCOMPLETE` if the entailment is incomplete (similar to `INVALID` but requiring less time to re-create), `INPROGRESS` if the entailment is being created, or `FAILED` if a system failure occurred during the creation of the entailment.
MODEL_COUNT	NUMBER	Number of models included in the entailment
RULEBASE_COUNT	NUMBER	Number of rulebases included in the entailment

Information about all database objects, such as models and rulebases, related to entailments is maintained in the MDSYS.SEM_RULES_INDEX_DATASETS view. This view has the columns shown in Table 1-7 and one row for each unique combination of values of all the columns.

Table 1-7 MDSYS.SEM_RULES_INDEX_DATASETS View Columns

Column Name	Data Type	Description
INDEX_NAME	VARCHAR2(25)	Name of the entailment
DATA_TYPE	VARCHAR2(8)	Type of data included in the entailment. Examples: `MODEL` and `RULEBASE`
DATA_NAME	VARCHAR2(25)	Name of the object of the type in the DATA_TYPE column

Example 1-3 creates an entailment named family_rb_rix_family, using the family model and the RDFS and family_rb rulebases. (This example is an excerpt from Example 1-110 in Example: Family Information.)

Example 1-3 Creating an Entailment

BEGIN
  SEM_APIS.CREATE_ENTAILMENT(
    'rdfs_rix_family',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'));
END;
/

Parent topic: Semantic Data in the Database

1.3.8 Virtual Models

A virtual model is a logical graph that can be used in a SEM_MATCH query. A virtual model is the result of a UNION or UNION ALL operation on one or more models and/or entailments.

Using a virtual model can provide several benefits:

It can simplify management of access privileges for semantic data. For example, assume that you have created three semantic models and one entailment based on the three models and the OWLPrime rulebase. Without a virtual model, you must individually grant and revoke access privileges for each model and the entailment. However, if you create a virtual model that contains the three models and the entailment, you will only need to grant and revoke access privileges for the single virtual model.
It can facilitate rapid updates to semantic models. For example, assume that virtual model VM1 contains model M1 and entailment R1 (that is, VM1 = M1 UNION ALL R1), and assume that semantic model M1_UPD is a copy of M1 that has been updated with additional triples and that R1_UPD is an entailment created for M1_UPD. Now, to have user queries over VM1 go to the updated model and entailment, you can redefine virtual model VM1 (that is, VM1 = M1_UPD UNION ALL R1_UPD).
It can simplify query specification because querying a virtual model is equivalent to querying multiple models in a SEM_MATCH query. For example, assume that models m1, m2, and m3 already exist, and that an entailment has been created for m1, m2 ,and m3 using the OWLPrime rulebase. You could create a virtual model vm1 as follows:
```
EXECUTE sem_apis.create_virtual_model('vm1', sem_models('m1', 'm2', 'm3'), 
                                      sem_rulebases('OWLPRIME'));
```
To query the virtual model, use the virtual model name as if it were a model in a SEM_MATCH query. For example, the following query on the virtual model:
```
SELECT * FROM TABLE (sem_match('{…}', sem_models('vm1'), null, …));
```
is equivalent to the following query on all the individual models:
```
SELECT * FROM TABLE (sem_match('{…}', sem_models('m1', 'm2', 'm3'), 
                                      sem_rulebases('OWLPRIME'), …));
```
A SEM_MATCH query over a virtual model will query either the SEMV or SEMU view (SEMU by default and SEMV if the 'ALLOW_DUP=T' option is specified) rather than querying the UNION or UNION ALL of each model and entailment. For information about these views and options, see the reference section for the SEM_APIS.CREATE_VIRTUAL_MODEL procedure.

Virtual models use views (described later in this section) and add some metadata entries, but do not significantly increase system storage requirements.

To create a virtual model, use the SEM_APIS.CREATE_VIRTUAL_MODEL procedure. To drop (delete) a virtual model, use the SEM_APIS.DROP_VIRTUAL_MODEL procedure. A virtual model is dropped automatically if any of its component models, rulebases, or entailment are dropped. To replace a virtual model without dropping it, use the SEM_APIS.CREATE_VIRTUAL_MODEL procedure with the REPLACE=T option. Replacing a virtual model allows you to redefine it while maintaining any access privileges.

To query a virtual model, specify the virtual model name in the models parameter of the SEM_MATCH table function, as shown in Example 1-4.

For information about the SEM_MATCH table function, see Using the SEM_MATCH Table Function to Query Semantic Data, which includes information using certain attributes when querying a virtual model.

When you create a virtual model, an entry is created for it in the MDSYS.SEM_MODEL$ view, which is described in Table 1-1 in Metadata for Models. However, the values in several of the columns are different for virtual models as opposed to semantic models, as explained in Table 1-8.

Table 1-8 MDSYS.SEM_MODEL$ View Column Explanations for Virtual Models

Column Name	Data Type	Description
OWNER	VARCHAR2(30)	Schema of the owner of the virtual model
MODEL_ID	NUMBER	Unique model ID number, automatically generated. Will be a negative number, to indicate that this is a virtual model.
MODEL_NAME	VARCHAR2(25)	Name of the virtual model
TABLE_NAME	VARCHAR2(30)	Null for a virtual model
COLUMN_NAME	VARCHAR2(30)	Null for a virtual model
MODEL_TABLESPACE_NAME	VARCHAR2(30)	Null for a virtual model

Information about all virtual models is maintained in the MDSYS.SEM_VMODEL_INFO view, which has the columns shown in Table 1-9 and one row for each virtual model.

Table 1-9 MDSYS.SEM_VMODEL_INFO View Columns

Column Name	Data Type	Description
OWNER	VARCHAR2(30)	Owner of the virtual model
VIRTUAL_MODEL_NAME	VARCHAR2(25)	Name of the virtual model
UNIQUE_VIEW_NAME	VARCHAR2(30)	Name of the view that contains unique triples in the virtual model, or null if the view was not created
DUPLICATE_VIEW_NAME	VARCHAR2(30)	Name of the view that contains duplicate triples (if any) in the virtual model
STATUS	VARCHAR2(30)	Contains `VALID` if the associated entailment is valid, `INVALID` if the entailment is not valid, `INCOMPLETE` if the entailment is incomplete (similar to `INVALID` but requiring less time to re-create), `INPROGRESS` if the entailment is being created, `FAILED` if a system failure occurred during the creation of the entailment, or `NORIDX` if no entailment is associated with the virtual model. In the case of multiple entailments, the lowest status among all of the component entailments is used as the virtual model's status (`INVALID` < `INCOMPLETE` < `VALID`).
MODEL_COUNT	NUMBER	Number of models in the virtual model
RULEBASE_COUNT	NUMBER	Number of rulebases used for the virtual model
RULES_INDEX_COUNT	NUMBER	Number of entailments in the virtual model

Information about all objects (models, rulebases, and entailments) related to virtual models is maintained in the MDSYS.SEM_VMODEL_DATASETS view. This view has the columns shown in Table 1-10 and one row for each unique combination of values of all the columns.

Table 1-10 MDSYS.SEM_VMODEL_DATASETS View Columns

Column Name	Data Type	Description
VIRTUAL_MODEL_NAME	VARCHAR2(25)	Name of the virtual model
DATA_TYPE	VARCHAR2(8)	Type of object included in the virtual model. Examples: `MODEL` for a semantic model, `RULEBASE` for a rulebase, or `RULEIDX` for an entailment
DATA_NAME	VARCHAR2(25)	Name of the object of the type in the DATA_TYPE column

Example 1-4 Querying a Virtual Model

SELECT COUNT(protein)
  FROM TABLE (SEM_MATCH (
    '{?protein rdf:type :Protein .
      ?protein :citation ?citation . 
      ?citation :author "Bairoch A."}',
    SEM_MODELS('UNIPROT_VM'), 
    NULL, 
    SEM_ALIASES(SEM_ALIAS('', 'http://purl.uniprot.org/core/')),
    NULL, 
    NULL, 
    'ALLOW_DUP=T'));

Parent topic: Semantic Data in the Database

1.3.9 Named Graphs

RDF Semantic Graph supports the use of named graphs, which are described in the "RDF Dataset" section of the W3C SPARQL Query Language for RDF recommendation (http://www.w3.org/TR/rdf-sparql-query/#rdfDataset).

This support is provided by extending an RDF triple consisting of the traditional subject, predicate, and object, to include an additional component to represent a graph name. The extended RDF triple, despite having four components, will continue to be referred to as an RDF triple in this document. In addition, the following terms are sometimes used:

N-Triple is a format that does not allow extended triples. Thus, n-triples can include only triples with three components.
N-Quad is a format that allows both "regular" triples (three components) and extended triples (four components, including the graph name). For more information, see http://www.w3.org/TR/2013/NOTE-n-quads-20130409/.

To load a file containing extended triples (possibly mixed with regular triples) into an Oracle database, the input file must be in N-Quad format.

The graph name component of an RDF triple must either be null or a URI. If it is null, the RDF triple is said to belong to a default graph; otherwise it is said to belong to a named graph whose name is designated by the URI.

Additionally, to support named graphs in SDO_RDF_TRIPLE_S object type (described in Semantic Data Types_ Constructors_ and Methods), a new syntax is provided for specifying a model-graph, that is, a combination of model and graph (if any) together, and the RDF_M_ID attribute holds the identifier for a model-graph: a combination of model ID and value ID for the graph (if any). The name of a model-graph is specified as model_name, and if a graph is present, followed by the colon (:) separator character and the graph name (which must be a URI and enclosed within angle brackets < >).

For example, in a medical data set the named graph component for each RDF triple might be a URI based on patient identifier, so there could be as many named graphs as there are unique patients, with each named graph consisting of data for a specific patient.

For information about performing specific operations with named graphs, see the following:

Using constructors and methods: Semantic Data Types_ Constructors_ and Methods
Loading: Loading N-Quad Format Data into a Staging Table Using an External Table and Loading Data into Named Graphs Using INSERT Statements
Querying: GRAPH Keyword Support and Expressions in the SELECT Clause
Inferencing: Using Named Graph Based Inferencing (Global and Local)

Data Formats Related to Named Graph Support

Parent topic: Semantic Data in the Database

1.3.9.1 Data Formats Related to Named Graph Support

TriG and N-QUADS are two popular data formats that provide graph names (or context) to triple data. (As of November 2011, neither format was a standard.) The graph names (context) can be used in a variety of different ways. Typical usage includes, but is not limited to, the grouping of triples for ease of management, localized query, localized inference, and provenance.

Example 1-5 RDF Data Encoded in TriG Format

Example 1-5 shows an RDF data set encoded in TriG format. It contains a default graph and a named graph.

@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix dc: <http://purl.org/dc/elements/1.1/> .
 
# Default graph
{
  <http://my.com/John> dc:publisher <http://publisher/Xyz> .
}
 
# A named graph
<http://my.com/John> {
  <http://my.com/John> foaf:name "John Doe" .
}

When loading the TriG file from Example 1-5 into a DatasetGraphOracleSem object (for example, using Example 6-12 in Bulk Loading Using RDF Semantic Graph Support for Apache Jena, but replacing the constant "N-QUADS" with "TRIG"), the triples in the default graph will be loaded into Oracle Database as triples with null graph names, and the triples in the named graphs will be loaded into Oracle Database with the designated graph names.

Example 1-6 N-QUADS Format Representation

N-QUADS format is a simple extension of the existing N-TRIPLES format by adding an optional fourth column (graph name or context). Example 1-6 shows the N-QUADS format representation of the TriG file from Example 1-5.

<http://my.com/John> <http://purl.org/dc/elements/1.1/publisher> <http://publisher/Xyz> .
<http://my.com/John> <http://xmlns.com/foaf/0.1/name> "John Doe" <http://my.com/John>

When loading an N-QUADS file into a DatasetGraphOracleSem object (see Example 6-12), lines without the fourth column will be loaded into Oracle Database as triples with null graph names, and lines with a fourth column will be loaded into Oracle Database with the designated graph names.

Parent topic: Named Graphs

1.3.10 Semantic Data Security Considerations

The following database security considerations apply to the use of semantic data:

When a model or entailment is created, the owner gets the SELECT privilege with the GRANT option on the associated view. Users that have the SELECT privilege on these views can perform SEM_MATCH queries against the associated model or entailment.
When a rulebase is created, the owner gets the SELECT, INSERT, UPDATE, and DELETE privileges on the rulebase, with the GRANT option. Users that have the SELECT privilege on a rulebase can create an entailment that includes the rulebase. The INSERT, UPDATE, and DELETE privileges control which users can modify the rulebase and how they can modify it.
To perform data manipulation language (DML) operations on a model, a user must have DML privileges for the corresponding base table.
The creator of the base table corresponding to a model can grant privileges to other users.
To perform data manipulation language (DML) operations on a rulebase, a user must have the appropriate privileges on the corresponding database view.
The creator of a model can grant SELECT privileges on the corresponding database view to other users.
A user can query only those models for which that user has SELECT privileges to the corresponding database views.
Only the creator of a model or a rulebase can drop it.

Parent topic: Semantic Data in the Database

1.4 Semantic Metadata Tables and Views

Oracle Database maintains several tables and views in the MDSYS schema to hold metadata related to semantic data.

Some of these tables and views are created by the SEM_APIS.CREATE_SEM_NETWORK procedure, as explained in Quick Start for Using Semantic Data, and some are created only as needed.Table 1-11 lists the tables and views in alphabetical order. (In addition, several tables and views are created for Oracle internal use, and these are accessible only by users with DBA privileges.)

Table 1-11 Semantic Metadata Tables and Views

Name	Contains Information About	Described In
RDF_CRS_URI$	Availavle EPSG spatial reference system URIs	Spatial Support
RDF_VALUE$	Subjects, properties, and objects used to represent statements	Statements
RDFOLS_SECURE_RESOURCE	Resources secured with Oracle Label Security (OLS) policies and the sensitivity labels associated with these resources	RDFOLS_SECURE_RESOURCE View
SEM_DTYPE_INDEX_INFO	All data type indexes in the network	Using Data Type Indexes
SEM_MODEL$	All models defined in the database	Metadata for Models
SEM_NETWORK_INDEX_INFO$	Semantic network indexes	MDSYS.SEM_NETWORK_INDEX_INFO View
SEM_RULEBASE_INFO	Rulebases	Inferencing: Rules and Rulebases
SEM_RULES_INDEX_DATASETS	Database objects used in entailments	Entailments (Rules Indexes)
SEM_RULES_INDEX_INFO	Entailments (rules indexes)	Entailments (Rules Indexes)
SEM_VMODEL_INFO	Virtual models	Virtual Models
SEM_VMODEL_DATASETS	Database objects used in virtual models	Virtual Models
SEMCL_entailment-name	`owl:sameAs` clique members and canonical representatives	Optimizing owl:sameAs Inference
SEMI_entailment-name	Triples in the specified entailment	Entailments (Rules Indexes)
SEMM_model-name	Triples in the specified model	Metadata for Models
SEMR_rulebase-name	Rules in the specified rulebase	Inferencing: Rules and Rulebases
SEMU_virtual-model-name	Unique triples in the virtual model	Virtual Models
SEMV_virtual-model-name	Triples in the virtual model	Virtual Models

Parent topic: RDF Semantic Graph Overview

1.5 Semantic Data Types, Constructors, and Methods

The SDO_RDF_TRIPLE object type represents semantic data in triple format, and the SDO_RDF_TRIPLE_S object type (the _S for storage) stores persistent semantic data in the database.

The SDO_RDF_TRIPLE_S type has references to the data, because the actual semantic data is stored only in the central RDF schema. This type has methods to retrieve the entire triple or part of the triple.

Note:

Blank nodes are always reused within an RDF model and cannot be reused across models

The SDO_RDF_TRIPLE type is used to display triples, whereas the SDO_RDF_TRIPLE_S type is used to store the triples in database tables.

The SDO_RDF_TRIPLE object type has the following attributes:

SDO_RDF_TRIPLE (
  subject VARCHAR2(4000), 
  property VARCHAR2(4000), 
  object VARCHAR2(10000))

The SDO_RDF_TRIPLE_S object type has the following attributes:

SDO_RDF_TRIPLE_S (
  RDF_C_ID NUMBER, -- Canonical object value ID
  RDF_M_ID NUMBER, -- Model (or Model-Graph) ID 
  RDF_S_ID NUMBER, -- Subject value ID
  RDF_P_ID NUMBER, -- Property value ID
  RDF_O_ID NUMBER) -- Object value ID

The SDO_RDF_TRIPLE_S type has the following methods that retrieve the name of the RDF model (or model-graph), a triple, or a part (subject, property, or object) of a triple:

GET_MODEL() RETURNS VARCHAR2
GET_TRIPLE() RETURNS SDO_RDF_TRIPLE
GET_SUBJECT() RETURNS VARCHAR2
GET_PROPERTY() RETURNS VARCHAR2
GET_OBJECT() RETURNS CLOB

Example 1-7 shows some of the SDO_RDF_TRIPLE_S methods.

Example 1-7 SDO_RDF_TRIPLE_S Methods

-- Find all articles that reference Article2.
SELECT a.triple.GET_SUBJECT() AS subject
    FROM articles_rdf_data a
    WHERE a.triple.GET_PROPERTY() = '<http://purl.org/dc/terms/references>' AND
    TO_CHAR(a.triple.GET_OBJECT()) = '<http://nature.example.com/Article2>';

SUBJECT                                                                         
--------------------------------------------------------------------------------
<http://nature.example.com/Article1>                                            

-- Find all triples with Article1 as subject.
SELECT a.triple.GET_TRIPLE() AS triple
    FROM articles_rdf_data a
    WHERE a.triple.GET_SUBJECT() = '<http://nature.example.com/Article1>';

TRIPLE(SUBJECT, PROPERTY, OBJECT)                                               
--------------------------------------------------------------------------------
SDO_RDF_TRIPLE('<http://nature.example.com/Article1>', '<http://purl.org/dc/elem
ents/1.1/title>', '"All about XYZ"')                                            
                                                                                
SDO_RDF_TRIPLE('<http://nature.example.com/Article1>', '<http://purl.org/dc/elem
ents/1.1/creator>', '"Jane Smith"')                                             
                                                                                
SDO_RDF_TRIPLE('<http://nature.example.com/Article1>', '<http://purl.org/dc/term
s/references>', '<http://nature.example.com/Article2>')                         
                                                                                
SDO_RDF_TRIPLE('<http://nature.example.com/Article1>', '<http://purl.org/dc/term
s/references>', '<http://nature.example.com/Article3>')                         

TRIPLE(SUBJECT, PROPERTY, OBJECT)                                               
--------------------------------------------------------------------------------
                                                                                
-- Find all objects where the subject is Article1.
SELECT a.triple.GET_OBJECT() AS object
    FROM articles_rdf_data a
    WHERE a.triple.GET_SUBJECT() = '<http://nature.example.com/Article1>';

OBJECT                                                                          
--------------------------------------------------------------------------------
"All about XYZ"                                                                 
"Jane Smith"                                                                    
<http://nature.example.com/Article2>                                            
<http://nature.example.com/Article3>                                            

-- Find all triples where Jane Smith is the object.
SELECT a.triple.GET_TRIPLE() AS triple
    FROM articles_rdf_data a
    WHERE TO_CHAR(a.triple.GET_OBJECT()) = '"Jane Smith"';

TRIPLE(SUBJECT, PROPERTY, OBJECT)                                               
--------------------------------------------------------------------------------
SDO_RDF_TRIPLE('<http://nature.example.com/Article1>', '<http://purl.org/dc/elem
ents/1.1/creator>', '"Jane Smith"')

Constructors for Inserting Triples

Parent topic: RDF Semantic Graph Overview

1.5.1 Constructors for Inserting Triples

The following constructor formats are available for inserting triples into a model table. The only difference is that in the second format the data type for the object is CLOB, to accommodate very long literals.

SDO_RDF_TRIPLE_S (
  model_name VARCHAR2, -- Model name
  subject    VARCHAR2, -- Subject
  property   VARCHAR2, -- Property
  object     VARCHAR2) -- Object
  RETURN     SELF;

SDO_RDF_TRIPLE_S (
  model_name VARCHAR2, -- Model name
  subject    VARCHAR2, -- Subject
  property   VARCHAR2, -- Property
  object     CLOB) -- Object
  RETURN SELF;

GET_OBJ_VALUE() RETURN VARCHAR2;

Example 1-8 uses the first constructor format to insert several triples.

Example 1-8 SDO_RDF_TRIPLE_S Constructor to Insert Triples

INSERT INTO articles_rdf_data VALUES (
  SDO_RDF_TRIPLE_S ('articles','<http://nature.example.com/Article1>',
    '<http://purl.org/dc/elements/1.1/creator>',
    '"Jane Smith"'));

INSERT INTO articles_rdf_data VALUES (
  SDO_RDF_TRIPLE_S ('articles:<http://examples.com/ns#Graph1>',
    '<http://nature.example.com/Article102>',
    '<http://purl.org/dc/elements/1.1/creator>',
    '_:b1'));
 
INSERT INTO articles_rdf_data VALUES (
  SDO_RDF_TRIPLE_S ('articles:<http://examples.com/ns#Graph1>',
    '_:b2',
    '<http://purl.org/dc/elements/1.1/creator>',
    '_:b1'));

Parent topic: Semantic Data Types, Constructors, and Methods

1.6 Using the SEM_MATCH Table Function to Query Semantic Data

To query semantic data, use the SEM_MATCH table function.

This function has the following attributes:

SEM_MATCH(
  query        VARCHAR2,
  models       SEM_MODELS,
  rulebases    SEM_RULEBASES,
  aliases      SEM_ALIASES,
  filter       VARCHAR2,
  index_status VARCHAR2,
  options      VARCHAR2,
  graphs       SEM_GRAPHS,
  named_graphs SEM_GRAPHS
 ) RETURN ANYDATASET;

The query attribute is required. The other attributes are optional (that is, each can be a null value).

The query attribute is a string literal (or concatenation of string literals) with one or more triple patterns, usually containing variables. (The query attribute cannot be a bind variable or an expression involving a bind variable.) A triple pattern is a triple of atoms followed by a period. Each atom can be a variable (for example, ?x), a qualified name (for example, rdf:type) that is expanded based on the default namespaces and the value of the aliases attribute, or a full URI (for example, <http://www.example.org/family/Male>). In addition, the third atom can be a numeric literal (for example, 3.14), a plain literal (for example, "Herman"), a language-tagged plain literal (for example, "Herman"@en), or a typed literal (for example, "123"^^xsd:int).

For example, the following query attribute specifies three triple patterns to find grandfathers (that is, grandparents who are also male) and the height of each of their grandchildren:

'{ ?x :grandParentOf ?y . ?x rdf:type :Male . ?y :height ?h }'

The models attribute identifies the model or models to use. Its data type is SEM_MODELS, which has the following definition: TABLE OF VARCHAR2(25). If you are querying a virtual model, specify only the name of the virtual model and no other models. (Virtual models are explained in Virtual Models.)

The rulebases attribute identifies one or more rulebases whose rules are to be applied to the query. Its data type is SDO_RDF_RULEBASES, which has the following definition: TABLE OF VARCHAR2(25). If you are querying a virtual model, this attribute must be null.

The aliases attribute identifies one or more namespaces, in addition to the default namespaces, to be used for expansion of qualified names in the query pattern. Its data type is SEM_ALIASES, which has the following definition: TABLE OF SEM_ALIAS, where each SEM_ALIAS element identifies a namespace ID and namespace value. The SEM_ALIAS data type has the following definition: (namespace_id VARCHAR2(30), namespace_val VARCHAR2(4000))

The following default namespaces (namespace_id and namespace_val attributes) are used by the SEM_MATCH table function and the SEM_CONTAINS and SEM_RELATED operators:

('ogc', 'http://www.opengis.net/ont/geosparql#')
('ogcf', 'http://www.opengis.net/def/function/geosparql/')
('ogcgml', 'http://www.opengis.net/ont/gml#')
('ogcsf', 'http://www.opengis.net/ont/sf#')
('orardf', 'http://xmlns.oracle.com/rdf/')
('orageo', 'http://xmlns.oracle.com/rdf/geo/')
('owl',    'http://www.w3.org/2002/07/owl#')
('rdf',    'http://www.w3.org/1999/02/22-rdf-syntax-ns#')
('rdfs',   'http://www.w3.org/2000/01/rdf-schema#')
('xsd',    'http://www.w3.org/2001/XMLSchema#')

You can override any of these defaults by specifying the namespace_id value and a different namespace_val value in the aliases attribute.

The filter attribute identifies any additional selection criteria. If this attribute is not null, it should be a string in the form of a WHERE clause without the WHERE keyword. For example: '(h >= ''6'')' to limit the result to cases where the height of the grandfather's grandchild is 6 or greater (using the example of triple patterns earlier in this section).

Note:

Instead of using the filter attribute, you are encouraged to use the FILTER keyword inside your query pattern whenever possible (as explained in Graph Patterns: Support for Curly Brace Syntax_ and OPTIONAL_ FILTER_ UNION_ and GRAPH Keywords). Using the FILTER keyword is likely to give better performance because of internal optimizations. The filter argument, however, can be useful if you require SQL constructs that cannot be expressed with the FILTER keyword.

The index_status attribute lets you query semantic data even when the relevant entailment does not have a valid status. (If you are querying a virtual model, this attribute refers to the entailment associated with the virtual model.) If this attribute is null, the query returns an error if the entailment does not have a valid status. If this attribute is not null, it must be the string INCOMPLETE or INVALID. For an explanation of query behavior with different index_status values, see Performing Queries with Incomplete or Invalid Entailments.

The options attribute identifies options that can affect the results of queries. Options are expressed as keyword-value pairs. The following options are supported:

ALL_AJ_HASH, ALL_AJ_MERGE, and ALL_BGP_NL are global query optimizer hints that specify that all anti joins for NOT EXISTS and MINUS operations should use the specified join type.
ALL_BGP_HASH and ALL_BGP_NL are global query optimizer hints that specify that all inter-BGP joins (for example. the join between the root BGP and an OPTIONAL BGP) should use the specified join type. (BGP stands for basic graph pattern. From the W3C SPARQL Query Language for RDF Recommendation: "SPARQL graph pattern matching is defined in terms of combining the results from matching basic graph patterns. A sequence of triple patterns interrupted by a filter comprises a single basic graph pattern. Any graph pattern terminates a basic graph pattern."

The BGP_JOIN(USE_NL) and BGP_JOIN(USE_HASH) HINT0 query optimizer hints can be used to control the join type with finer granularity.

Example 1-14 shows the ALL_BGP_HASH option used in a SEM_MATCH query.
ALL_LINK_HASH and ALL_LINK_NL are global query optimizer hints that specify the join type for all RDF_LINK$ joins (that is, all joins between triple patterns within a BGP). ALL_LINK_HASH and ALL_LINK_NL can also be used within a HINT0 query optimizer hint for finer granularity.
ALL_MAX_PP_DEPTH(n) is a global query optimizer hint that sets the maximum depth to use when evaluating * and + property path operators. The default value is 10. The MAX_PP_DEPTH(n) HINT0 hint can be used to specify maximum depth with finer granularity.
ALL_ORDERED is a global query optimizer hint that specifies that the triple patterns in each BGP in the query should be evaluated in order.

Example 1-14 shows the ALL_ORDERED option used in a SEM_MATCH query.
ALL_USE_PP_HASH and ALL_USE_PP_NL are global query optimizer hints that specify the join type to use when evaluating property path expressions. The USE_PP_HASH and USE_PP_NL HINT0 hints can be used for specifying join type with finer granularity.
ALLOW_DUP=T generates an underlying SQL statement that performs a "union all" instead of a union of the semantic models and inferred data (if applicable). This option may introduce more rows (duplicate triples) in the result set, and you may need to adjust the application logic accordingly. If you do not specify this option, duplicate triples are automatically removed across all the models and inferred data to maintain the set semantics of merged RDF graphs; however, removing duplicate triples increases query processing time. In general, specifying 'ALLOW_DUP=T' improves performance significantly when multiple semantic models are involved in a SEM_MATCH query.

If you are querying a virtual model, specifying ALLOW_DUP=T causes the SEMV_vm_name view to be queried; otherwise, the SEMU_vm_name view is queried.
ALLOW_PP_DUP=T allows duplicate results for + and * property path queries. Allowing duplicate results may return the first result rows faster.
AS_OF [SCN, <SCN_VALUE>] , where <SCN_VALUE> is a valid system change number, indicates that Flashback Query should be used to query the state of the semantic network as of the specified SCN.
AS_OF [TIMESTAMP, <TIMESTAMP_VALUE>] , where <TIMESTAMP_VALUE> is a valid timestamp string with format 'YYYY/MM/DD HH24:MI:SS.FF', indicates that Flashback Query should be used to query the state of the semantic network as of the specified timestamp.
CLOB_AGG_SUPPORT=T enables support for CLOB values for the following aggregates: MIN, MAX, GROUP_CONCAT, SAMPLE. Note that enabling CLOB support incurs a significant performance penalty.
CLOB_EXP_SUPPORT=T enables support for CLOB values for some built-in SPARQL functions. Note that enabling CLOB support incurs a significant performance penalty.
CONSTRUCT_STRICT=T eliminates invalid RDF triples from the result of SPARQL CONSTRUCT or SPARQL DESCRIBE syntax queries. RDF triples with literals in the subject position or literals or blank nodes in the predicate position are considered invalid.
CONSTRUCT_UNIQUE=T eliminates duplicate RDF triples from the result of SPARQL CONSTRUCT or SPARQL DESCRIBE syntax queries.
DISABLE_NULL_EXPR_JOIN specifies that the query compiler should assume that all SELECT expressions produce non-null output.
DO_UNESCAPE=T causes characters in the following return columns to be unescaped according to the W3C N-Triples specification (http://www.w3.org/TR/rdf-testcases/#ntriples): var, var$_PREFIX, var$_SUFFIX, var$RDFCLOB, var$RDFLTYP, var$RDFLANG, and var$RDFTERM.

See also the reference information for SEM_APIS.ESCAPE_CLOB_TERM, SEM_APIS.ESCAPE_CLOB_VALUE, SEM_APIS.ESCAPE_RDF_TERM, SEM_APIS.ESCAPE_RDF_VALUE, SEM_APIS.UNESCAPE_CLOB_TERM, SEM_APIS.UNESCAPE_CLOB_VALUE, SEM_APIS.UNESCAPE_RDF_TERM, and SEM_APIS.UNESCAPE_RDF_VALUE.
FINAL_VALUE_HASH and FINAL_VALUE_NL are global query optimizer hints that specify the join method that should be used to obtain the lexical values for any query variables that are not used in a FILTER clause.
GRAPH_MATCH_UNNAMED=T allows unnamed triples (null G_ID) to be matched inside GRAPH clauses. That is, two triples will satisfy the graph join condition if their graphs are equal or if one or both of the graphs are null. This option may be useful when your dataset includes unnamed TBOX triples or unnamed entailed triples.
HINT0={<hint-string>} (pronounced and written "hint" and the number zero) specifies one or more keywords with hints to influence the execution plan and results of queries. Conceptually, a graph pattern with n triple patterns and referring to m distinct variables results in an (n+m)-way join: n-way self-join of the target RDF model or models and optionally the corresponding entailment, and then m joins with RDF_VALUE$ for looking up the values for the m variables. A hint specification affects the join order and join type used for the query execution.

The hint specification, <hint-string>, uses keywords, some of which have parameters consisting of a sequence or set of aliases, or references, for individual triple patterns and variables used in the query. Aliases for triple patterns are of the form ti where i refers to the 0-based ordinal numbers of triple patterns in the query. For example, the alias for the first triple pattern in a query is t0, the alias for the second one is t1, and so on. Aliases for the variables used in a query are simply the names of those variables. Thus, ?x will be used in the hint specification as the alias for a variable ?x used in the graph pattern.

Hints used for influencing query execution plans include LEADING(<sequence of aliases>), USE_NL(<set of aliases>), USE_HASH(<set of aliases>), and INDEX(<alias> <index_name>). These hints have the same format and basic meaning as hints in SQL statements, which are explained in Oracle Database SQL Language Reference.

Example 1-10 shows the HINT0 option used in a SEM_MATCH query.
HTTP_METHOD=POST_PAR indicates that the HTTP POST method with URL-encoded parameters pass should be used for the SERVICE request. The default option for requests is the HTTP GET method. For more information about SPARQL protocol, see http://www.w3.org/TR/2013/REC-sparql11-protocol-20130321/#protocol.
INF_ONLY=T queries only the entailed graph for the specified models and rulebases.
OVERLOADED_NL=T specifies that a procedural nested loop execution should be used to join with an overloaded SERVICE clause.
PLUS_RDFT=T can be used with SPARQL SELECT syntax (see Expressions in the SELECT Clause) to additionally return a var$RDFTERM CLOB column for each projected query variable. The value for this column is equivalent to the result of SEM_APIS.COMPOSE_RDF_TERM(var, var$RDFVTYP, var$RDFLTYP, var$RDFLANG, var$RDFCLOB). When using this option, the return columns for each variable var will be var, var$RDFVID, var$_PREFIX, var$_SUFFIX, var$RDFVTYP, var$RDFCLOB, var$RDFLTYP, var$RDFLANG, and var$RDFTERM.
PLUS_RDFT=VC can be used with SPARQL SELECT syntax (see Expressions in the SELECT Clause) to additionally return a var$RDFTERM VARCHAR2(4000) column for each projected query variable. The value for this column is equivalent to the result of SEM_APIS.COMPOSE_RDF_TERM(var, var$RDFVTYP, var$RDFLTYP, var$RDFLANG). When using this option, the return columns for each variable var will be var, var$RDFVID, var$_PREFIX, var$_SUFFIX, var$RDFVTYP, var$RDFCLOB, var$RDFLTYP, var$RDFLANG, and var$RDFTERM.
PROJ_EXACT_VALUES=T disables canonicalization of values returned from functions and of constant values used in value assignment statements. Such values are canonicalized by default.
SERVICE_CLOB=F sets the column values of var$RDFCLOB to null instead of saving values when calling the service. If CLOB data is not needed in your application, performance can be improved by using this option to skip CLOB processing.
SERVICE_ESCAPE=F disables character escaping for RDF literal values returned by SPARQL SERVICE calls. RDF literal values are escaped by default. If character escaping is not relevant for your application, performance can be improved by disabling character escaping.
SERVICE_JPDWN=T is a query optimizer hint for using nested loop join in SPARQL SERVICE. Example 1-70 shows the SERVICE_JPDWN=T option used in a SEM_MATCH query.
SERVICE_PROXY=<proxy-string> sets a proxy address to be used when performing http connections. The given proxy-string will be used in SERVICE queries. Example 1-73 shows a SEM_MATCH query including a proxy address.
STRICT_AGG_CARD=T uses SPARQL semantics (one null row) instead of SQL semantics (zero rows) for aggregate queries with graph patterns that fail to match. This option incurs a slight performance penalty.
STRICT_DEFAULT=T restricts the default graph to unnamed triples when no dataset information is specified.

The graphs attribute specifies the set of named graphs from which to construct the default graph for a SEM_MACH query. Its data type is SEM_GRAPHS, which has the following definition: TABLE OF VARCHAR2(4000). The default value for this attribute is NULL. When graphs is NULL, the "union all" of all graphs in the set of query models is used as the default graph.

The named_graphs attribute specifies the set of named graphs that can be matched by a GRAPH clause. Its data type is SEM_GRAPHS, which has the following definition: TABLE OF VARCHAR2(4000). The default value for this attribute is NULL. When named_graphs is NULL, all named graphs in the set of query models can be matched by a GRAPH clause.

The SEM_MATCH table function returns an object of type ANYDATASET, with elements that depend on the input variables. In the following explanations, var represents the name of a variable used in the query. For each variable var, the result elements have the following attributes: var, var$RDFVID, var$_PREFIX, var$_SUFFIX, var$RDFVTYP, var$RDFCLOB, var$RDFLTYP, and var$RDFLANG.

In such cases, var has the lexical value bound to the variable, var$RDFVID has the VALUE_ID of the value bound to the variable, var$_PREFIX and var$_SUFFIX are the prefix and suffix of the value bound to the variable, var$RDFVTYP indicates the type of value bound to the variable (URI, LIT [literal], or BLN [blank node]), var$RDFCLOB has the lexical value bound to the variable if the value is a long literal, var$RDFLTYP indicates the type of literal bound if a literal is bound, and var$RDFLANG has the language tag of the bound literal if a literal with language tag is bound. var$RDFCLOB is of type CLOB, while all other attributes are of type VARCHAR2.

For a literal value or a blank node, its prefix is the value itself and its suffix is null. For a URI value, its prefix is the left portion of the value up to and including the rightmost occurrence of any of the three characters / (slash), # (pound), or : (colon), and its suffix is the remaining portion of the value to the right. For example, the prefix and suffix for the URI value http://www.example.org/family/grandParentOf are http://www.example.org/family/ and grandParentOf, respectively.

Along with columns for variable values, a SEM_MATCH query that uses SPARQL SELECT syntax returns one additional NUMBER column, SEM$ROWNUM, which can be used to ensure the correct result ordering for queries that involve a SPARQL ORDER BY clause.

A SEM_MATCH query that uses SPARQL ASK syntax returns the columns ASK, ASK$RDFVID, ASK$_PREFIX, ASK$_SUFFIX, ASK$RDFVTYP, ASK$RDFCLOB, ASK$RDFLTYP, ASK$RDFLANG, and SEM$ROWNUM. This is equivalent to a SPARQL SELECT syntax query that projects a single ?ask variable.

A SEM_MATCH query that uses SPARQL CONSTRUCT or SPARQL DESCRIBE syntax returns columns that contain RDF triple data rather than query result bindings. Such queries return values for subject, predicate and object components. See Graph Patterns: Support for SPARQL CONSTRUCT Syntaxfor details.

To use the SEM_RELATED operator to query an OWL ontology, see Using Semantic Operators to Query Relational Data.

When you are querying multiple models or querying one or more models and the corresponding entailment, consider using virtual models (explained in Virtual Models) because of the potential performance benefits.

Example 1-9 SEM_MATCH Table Function

Example 1-9 selects all grandfathers (grandparents who are male) and their grandchildren from the family model, using inferencing from both the RDFS and family_rb rulebases. (This example is an excerpt from Example 1-110 in Example: Family Information.)

SELECT x grandfather, y grandchild
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male}',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

Example 1-10 HINT0 Option with SEM_MATCH Table Function

Example 1-10 is functionally the same as Example 1-9, but it adds the HINT0 option.

SELECT x grandfather, y grandchild
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male}',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_Aliases(SEM_ALIAS('','http://www.example.org/family/')),
    null,
    null,
    'HINT0={LEADING(t0 t1) USE_NL(?x ?y) GET_CANON_VALUE(?x ?y)}'));

Example 1-11 SEM_MATCH Table Function

Example 1-11 uses the Pathway/Genome BioPax ontology to get all chemical compound types that belong to both Proteins and Complexes:

SELECT t.r 
  FROM TABLE (SEM_MATCH ( 
      '{?r rdfs:subClassOf :Proteins .  
        ?r rdfs:subClassOf :Complexes}', 
      SEM_Models ('BioPax'), 
      SEM_Rulebases ('rdfs'), 
      SEM_Aliases (SEM_ALIAS('', 'http://www.biopax.org/release1/biopax-release1.owl')),
      NULL)) t;

As shown in Example 1-11, the search pattern for the SEM_MATCH table function is specified using SPARQL-like syntax where the variable starts with the question-mark character (?). In this example, the variable ?r must match to the same term, and thus it must be a subclass of both Proteins and Complexes.

Parent topic: RDF Semantic Graph Overview

1.6.1 Performing Queries with Incomplete or Invalid Entailments

You can query semantic data even when the relevant entailment does not have a valid status if you specify the string value INCOMPLETE or INVALID for the index_status attribute of the SEM_MATCH table function. (The entailment status is stored in the STATUS column of the MDSYS.SEM_RULES_INDEX_INFO view, which is described in Entailments (Rules Indexes). The SEM_MATCH table function is described in Using the SEM_MATCH Table Function to Query Semantic Data.)

The index_status attribute value affects the query behavior as follows:

If the entailment has a valid status, the query behavior is not affected by the value of the index_status attribute.
If you provide no value or specify a null value for index_status, the query returns an error if the entailment does not have a valid status.
If you specify the string INCOMPLETE for the index_status attribute, the query is performed if the status of the entailment is incomplete or valid.
If you specify the string INVALID for the index_status attribute, the query is performed regardless of the actual status of the entailment (invalid, incomplete, or valid).

However, the following considerations apply if the status of the entailment is incomplete or invalid:

If the status is incomplete, the content of an entailment may be approximate, because some triples that are inferable (due to the recent insertions into the underlying models) may not actually be present in the entailment, and therefore results returned by the query may be inaccurate.
If the status is invalid, the content of the entailment may be approximate, because some triples that are no longer inferable (due to recent modifications to the underlying models or rulebases, or both) may still be present in the entailment, and this may affect the accuracy of the result returned by the query. In addition to possible presence of triples that are no longer inferable, some inferable rows may not actually be present in the entailment.

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.2 Graph Patterns: Support for Curly Brace Syntax, and OPTIONAL, FILTER, UNION, and GRAPH Keywords

The SEM_MATCH table function accepts the syntax for the graph pattern in which a sequence of triple patterns is enclosed within curly braces. The period is usually required as a separator unless followed by the OPTIONAL, FILTER, UNION, or GRAPH keyword. With this syntax, you can do any combination of the following:

Use the OPTIONAL construct to retrieve results even in the case of a partial match
Use the FILTER construct to specify a filter expression in the graph pattern to restrict the solutions to a query
Use the UNION construct to match one of multiple alternative graph patterns
Use the GRAPH construct (explained in GRAPH Keyword Support) to scope graph pattern matching to a set of named graphs

In addition to arithmetic operators (+, -, *, /), Boolean operators and logical connectives (||, &&, !), and comparison operators (<, >, <=, >=, =, !=), several built-in functions are available for use in FILTER clauses. Table 1-12 lists built-in functions that you can use in the FILTER clause. In the Description column of Table 1-12, x, y, and z are arguments of the appropriate types.

Table 1-12 Built-in Functions Available for FILTER Clause

Function	Description
ABS(RDF term)	Returns the absolute value of `term`. If `term` is a non-numerical value, returns null.
BNODE(literal) or BNODE()	Constructs a blank node that is distinct from all blank nodes in the dataset of the query, and those created by this function in other queries. The form with no arguments results in a distinct blank node in every call. The form with a simple literal results in distinct blank nodes for different simple literals, and the same blank node for calls with the same simple literal.
BOUND(variable)	BOUND(x) returns `true` if `x` is bound (that is, non-null) in the result, `false` otherwise.
CEIL(RDF term)	Returns the closest number with no fractional part which is not less than term. If term is a non-numerical value, returns null.
COALESCE(term list)	Returns the first element on the argument list that is evaluated without raising an error. Unbound variables raise an error if evaluated. Returns null if there are no valid elements in the term list.
CONCAT(term list)	Returns an `xsd:String` value resulting of the concatenation of the string values in the term list.
CONTAINS(literal, match)	Returns `true` if the string `match` is found anywhere in `literal`. It returns `false` otherwise.
DATATYPE(literal)	DATATYPE(x) returns a URI representing the datatype of `x`.
DAY(argument)	Returns an integer corresponding to the day part of argument. If the argument is not a `dateTime` or `date` data type, it returns a null value.
ENCODE_FOR_URI(literal)	Returns a string where the reserved characters in `literal` are escaped and converted to its percent-encode form.
EXISTS(pattern)	Returns `true` if the pattern matches the query data set, using the current bindings in the containing group graph pattern and the current active graph. If there are no matches, it returns `false`.
FLOOR(RDF term)	Returns the closest number with no fractional part which is less than `term`. If `term` is a non-numerical value, returns null.
HOURS(argument)	Returns an integer corresponding to the hours part of `argument`. If the argument is not a `dateTime` or `date` data type, it returns a null value.
IF(condition , expression1, expression2)	Evaluates the condition and obtains the effective Boolean value. If true, the first expression is evaluated and its value returned. If false, the second expression is used. If the condition raises an error, the error is passed as the result of the IF statement.
IRI(RDF term)	Returns an IRI resolving the string representation of argument `term`. If there is a base IRI defined in the query, the IR is resolve against it, and the result must result in an absolute IRI.
isBLANK(RDF term)	isBLANK(x) returns `true` if `x` is a blank node, `false` otherwise.
isIRI(RDF term)	isIRI(x) returns `true` if `x` is an IRI, `false` otherwise.
isLITERAL(RDF term)	isLiteral(x) returns `true` if `x` is a literal, `false` otherwise.
IsNUMERIC(RDF term)	Returns `true` if `term` is a numeric value, `false` otherwise.
isURI(RDF term)	isURI(x) returns `true` if `x` is a URI, `false` otherwise.
LANG(literal)	LANG(x) returns a plain literal serializing the language tag of `x`.
LANGMATCHES(literal, literal)	LANGMATCHES(x, y) returns `true` if language tag `x` matches language range `y`, `false` otherwise.
LCASE(literal)	Returns a string where each character in literal is converted to its lowercase correspondent.
MD5(literal)	Returns the checksum for `literal`, corresponding to the MD5 hash function.
MINUTES(argument)	Returns an integer corresponding to the minutes part of `argument`. If the argument is not a `dateTime` or `date` data type, it returns a null value.
MONTH(argument)	Returns an integer corresponding to the month part of `argument`. If the argument is not a `dateTime` or `date` data type, it returns a null value.
NOT_EXISTS(pattern)	Returns `true` if the pattern does not match the query data set, using the current bindings in the containing group graph pattern and the current active graph. It returns `false` otherwise.
NOW()	Returns an `xsd:dateTime` value corresponding to the current time at the moment of the query execution.
RAND()	Generates a numeric value in the range of [0,1).
REGEX(string, pattern)	REGEX(x,y) returns `true` if `x` matches the regular expression `y`, `false` otherwise. For more information about the regular expressions supported, see the Oracle Regular Expression Support appendix in Oracle Database SQL Language Reference.
REGEX(string, pattern, flags)	REGEX(x,y,z) returns `true` if `x` matches the regular expression `y` using the options given in `z`, `false` otherwise. Available options: `'s'` – dot all mode (`'.'` matches any character including the newline character); `'m'` – multiline mode (`'^'` matches the beginning of any line and `'$'` matches the end of any line); `'i'` – case insensitive mode; `'x'` – remove whitespace characters from the regular expression before matching.
REPLACE(string, pattern, replacement)	Returns a string where each match of the regular expression `pattern` in `string` is replaced by `replacement`. For more information about the regular expressions supported, see the Oracle Regular Expression Support appendix in Oracle Database SQL Language Reference.
REPLACE(string, pattern, replacement, flags)	Returns a string where each match of the regular expression `pattern` in string is replaced by `replacement`. Available options: `'s'` – dot all mode (`'.'` matches any character including the newline character); `'m'` – multiline mode (`'^'` matches the beginning of any line and `'$'` matches the end of any line); `'i'` – case insensitive mode; `'x'` – remove whitespace characters from the regular expression before matching. For more information about the regular expressions supported, see the Oracle Regular Expression Support appendix in Oracle Database SQL Language Reference.
ROUND(RDF term)	Returns the closest number with no fractional part to `term`. If two values exist, the value closer to positive infinite is returned. If term is a non-numerical value, returns null.
sameTerm(RDF term, RDF term)	sameTerm(x, y) returns `true` if `x` and `y` are the same RDF term, `false` otherwise.
SECONDS(argument)	Returns an integer corresponding to the seconds part of `argument`. If the argument is not a `dateTime` or `date` data type, it returns a null value.
SHA1(literal)	Returns the checksum for `literal`, corresponding to the SHA1 hash function.
SHA256(literal)	Returns the checksum for `literal`, corresponding to the SHA256 hash function.
SHA384(literal)	Returns the checksum for `literal`, corresponding to the SHA384 hash function.
SHA512(literal)	Returns the checksum for `literal`, corresponding to the SHA512 hash function.
STR(RDF term)	STR(x) returns a plain literal of the string representation of `x` (that is, what would be stored in the VALUE_NAME column of MDSYS.RDF_VALUE$ enclosed within double quotes).
STRAFTER(literal, literal)	StrAfter (x,y) returns the portion of the string corresponding to substring that precedes in `x` the first match of `y`, and the end of `x`. If `y` cannot be matched inside `x`, the empty string is returned.
STRBEFORE(literal, literal)	StrBefore (x,y) returns the portion of the string corresponding to the start of `x` and the first match of `y`. If `y` cannot be matched inside `x`, the empty string is returned.
STRDT(string, datatype)	Construct a literal term composed by the `string` lexical form and the `datatype` passed as arguments. `datatype` must be a URI; otherwise, the function returns a null value.
STRENDS(literal, match)	Returns `true` if the string `literal` ends with the string `match`. It returns `false` otherwise.
STRLANG (string, languageTag)	Constructs a string composed by the `string` lexical form and language tag passed as arguments.
STRLEN(literal)	Returns the length of the lexical form of the `literal`.
STRSTARTS(literal, match)	Returns `true` if the string `literal` starts with the string `match`. It returns `false` otherwise.
STRUUID()	Returns a string containing the scheme section of a new UUID.
SUBSTR(term, startPos)	Returns the string corresponding to the portion of `term` that starts at `startPos` and continues until term ends. The index of the first character is 1.
SUBSTR(term, startPos, length)	Returns the string corresponding to the portion of term that starts at `startPos` and continues for `length` characters. The index of the first character is 1.
term IN (term list)	The expression x IN(term list) returns `true` if `x` can be found in any of the values in `termlist`. Returns `false` if not found. Zero-length lists are legal. An error is raised if any of the values in `termlist` raises an error and `x` is not found.
term NOT IN (term list)	The expression x NOT IN(term list) returns `false` if `x` can be found in any of the values in term list. Returns `true` if not found. Zero-length lists are legal. An error is raised if any of the values in term list raises an error and `x` is not found.
TIMEZONE(argument)	Returns the time zones section of `argument` as an `xsd:dayTimeDuration` value. If the argument is not a `dateTime` or `date` data type, it returns a null value.
TZ(argument)	Returns an integer corresponding to the time zone part of `argument`. If the argument is not a `dateTime` or `date` data type, it returns a null value.
UCASE(literal)	Returns a string where each character in `literal` is converted to its uppercase correspondent.
URI(RDF term)	(Synonym for IRI(RDF term)
UUID()	Returns a URI with a new Universal Unique Identifier. The value and the version correspond to the PL/SQL function `sys_guid ()`.
YEAR(argument)	Returns an integer corresponding to the year part of `argument`.

See also the descriptions of the built-in functions defined in the SPARQL query language specification (http://www.w3.org/TR/sparql11-query/), to better understand the built-in functions available in SEM_MATCH.

In addition, Oracle provides some proprietary query functions that take advantage of Oracle Database features and help improve query performance. The following table lists these Oracle-specific query functions. Note that the built-in namespace prefix orardf expands to <http://xmlns.oracle.com/rdf/>.

Table 1-13 Oracle-Specific Query Functions

Function	Description
orardf:like(RDF term, pattern)	Returns `true` if the given term matches with the given like pattern, `false` otherwise. See Full-Text Search for more information.
orardf:sameCanonTerm(RDF term, RDF term)	Returns `true` if two terms represent the same canonical RDF term, `false` otherwise. Allows a VALUE_ID-based comparison, which is more efficient than `sameTerm(?x, ?y) or (?x = ?y)`.
orardf:textContains(RDF term, pattern)	Returns `true` if the given term matches with the given Oracle Text search pattern, `false` otherwise. See Full-Text Search for more information.
orardf:textScore(invocation id)	Returns the score of an orardf:textContains match. See Full-Text Search for more information.
(Spatial built-in functions)	(See Spatial Support.)

The following XML Schema casting functions are available for use in FILTER clauses. These functions take an RDF term as input and return a new RDF term of the desired type or raise an error if the term cannot be cast to the desired type. Details of type casting can be found in Section 17.1 of the XPath query specification: http://www.w3.org/TR/xpath-functions/#casting-from-primitive-to-primitive. These functions use the XML Namespace xsd : http://www.w3.org/2001/XMLSchema#.

xsd:string (RDF term)
xsd:dateTime (RDF term)
xsd:boolean (RDF term)
xsd:integer (RDF term)
xsd:float (RDF term)
xsd:double (RDF term)
xsd:decimal (RDF term)

If you use the syntax with curly braces to express a graph pattern:

The query always returns canonical lexical forms for the matching values for the variables.
Any hints specified in the options argument using HINT0={<hint-string>} (explained in Using the SEM_MATCH Table Function to Query Semantic Data), should be constructed only on the basis of the portion of the graph pattern inside the root BGP. For example, the only valid aliases for use in a hint specification for the query in Example 1-13 are t0, t1, ?x, and ?y. Inline query optimizer hints can be used to influence other parts of the graph pattern (see Inline Query Optimizer Hints).
The FILTER construct is not supported for variables bound to long literals.

Example 1-12 Curly Brace Syntax

Example 1-12 uses the syntax with curly braces and a period to express a graph pattern in the SEM_MATCH table function.

SELECT x, y
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male}',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

Example 1-13 Curly Brace Syntax and OPTIONAL Construct

Example 1-13 uses the OPTIONAL construct to modify Example 1-12, so that it also returns, for each grandfather, the names of the games that he plays or null if he does not play any games.

SELECT x, y, game
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male . 
      OPTIONAL{?x :plays ?game} 
     }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null,
    null,
    'HINT0={LEADING(t0 t1) USE_NL(?x ?y)}'));

Example 1-14 Curly Brace Syntax and Multi-Pattern OPTIONAL Construct

When multiple triple patterns are present in an OPTIONAL graph pattern, values for optional variables are returned only if a match is found for each triple pattern in the OPTIONAL graph pattern. Example 1-14 modifies Example 1-13 so that it returns, for each grandfather, the names of the games both he and his grandchildren play, or null if he and his grandchildren have no such games in common. It also uses global query optimizer hints to specify that triple patterns should be evaluated in order within each BGP and that a hash join should be used to join the root BGP with the OPTIONAL BGP.

SELECT x, y, game
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male . 
      OPTIONAL{?x :plays ?game . ?y :plays ?game} 
     }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null,
    'ALL_ORDERED ALL_BGP_HASH'));

Example 1-15 Curly Brace Syntax and Nested OPTIONAL Construct

A single query can contain multiple OPTIONAL graph patterns, which can be nested or parallel. Example 1-15 modifies Example 1-14 with a nested OPTIONAL graph pattern. This example returns, for each grandfather, (1) the games he plays or null if he plays no games and (2) if he plays games, the ages of his grandchildren that play the same game, or null if he has no games in common with his grandchildren. Note that in Example 1-15 a value is returned for ?game even if the nested OPTIONAL graph pattern ?y :plays ?game . ?y :age ?age is not matched.

SELECT x, y, game, age
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male . 
      OPTIONAL{?x :plays ?game 
                          OPTIONAL {?y :plays ?game . ?y :age ?age} } 
     }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

Example 1-16 Curly Brace Syntax and Parallel OPTIONAL Construct

Example 1-16 modifies Example 1-14 with a parallel OPTIONAL graph pattern. This example returns, for each grandfather, (1) the games he plays or null if he plays no games and (2) his email address or null if he has no email address. Note that, unlike nested OPTIONAL graph patterns, parallel OPTIONAL graph patterns are treated independently. That is, if an email address is found, it will be returned regardless of whether or not a game was found; and if a game was found, it will be returned regardless of whether an email address was found.

SELECT x, y, game, email
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male . 
      OPTIONAL{?x :plays ?game}
      OPTIONAL{?x :email ?email} 
     }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

Example 1-17 Curly Brace Syntax and FILTER Construct

Example 1-17 uses the FILTER construct to modify Example 1-12, so that it returns grandchildren information for only those grandfathers who are residents of either NY or CA.

SELECT x, y
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male . ?x :residentOf ?z
       FILTER (?z = "NY"  || ?z = "CA")}',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

Example 1-18 Curly Brace Syntax and FILTER with REGEX and STR Built-In Constructs

Example 1-18 uses the REGEX built-in function to select all grandfathers who have an Oracle email address. Note that backslash (\) characters in the regular expression pattern must be escaped in the query string; for example, \\. produces the following pattern: \.

SELECT x, y, z
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male . ?x :email ?z
       FILTER (REGEX(STR(?z), "@oracle\\.com$"))}',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

Example 1-19 Curly Brace Syntax and UNION and FILTER Constructs

Example 1-19 uses the UNION construct to modify Example 1-17, so that grandfathers are returned only if they are residents of NY or CA or own property in NY or CA, or if both conditions are true (they reside in and own property in NY or CA).

SELECT x, y
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male 
       {{?x :residentOf ?z} UNION {?x :ownsPropertyIn ?z}}
       FILTER (?z = "NY"  || ?z = "CA")}',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

GRAPH Keyword Support

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.2.1 GRAPH Keyword Support

A SEM_MATCH query is executed against an RDF Dataset. An RDF Dataset is a collection of graphs that includes one unnamed graph, known as the default graph, and one or more named graphs, which are identified by a URI. Graph patterns that appear inside a GRAPH clause are matched against the set of named graphs, and graph patterns that do not appear inside a graph clause are matched against the default graph. The graphs and named_graphs SEM_MATCH parameters are used to construct the default graph and set of named graphs for a given SEM_MATCH query. A summary of possible dataset configurations is shown in Table 1-14.

Table 1-14 SEM_MATCH graphs and named_graphs Values, and Resulting Dataset Configurations

Parameter Values	Default Graph	Set of Named Graphs
`graphs`: NULL `named_graphs`: NULL	Union All of all unnamed triples and all named graph triples. (But if the `options` parameter contains `STRICT_DEFAULT=T`, only unnamed triples are included in the default graph.)	All named graphs
`graphs`: NULL `named_graphs`: {g1,…, gn}	Empty set	{g1,…, gn}
`graphs`: {g1,…, gm} `named_graphs`: NULL	Union All of {g1,…, gm}	Empty set
`graphs`: {g1,…, gm} `named_graphs`: {gn,…, gz}	Union All of {g1,…, gm}	{gn,…, gz}

See also the W3C SPARQL specification for more information on RDF data sets and the GRAPH construct, specifically: http://www.w3.org/TR/rdf-sparql-query/#rdfDataset

Example 1-20 Named Graph Construct

Example 1-20 uses the GRAPH construct to scope graph pattern matching to a specific named graph. This example finds the names and email addresses of all people in the <http://www.example.org/family/Smith> named graph.

SELECT name, email
  FROM TABLE(SEM_MATCH(
    '{GRAPH :Smith {
       ?x :name ?name . ?x :email ?email } }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

Example 1-21 Using the named_graphs Parameter

In addition to URIs, variables can appear after the GRAPH keyword. Example 1-21 uses a variable, ?g, with the GRAPH keyword, and uses the named_graphs parameter to restrict the possible values of ?g to the <http://www.example.org/family/Smith> and <http://www.example.org/family/Jones> named graphs. Aliases specified in SEM_ALIASES argument can be used in the graphs and named_graphs parameters.

SELECT name, email
  FROM TABLE(SEM_MATCH(
    '{GRAPH ?g {
       ?x :name ?name . ?x :email ?email } }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null,null,null,null,
    SEM_GRAPHS('<http://www.example.org/family/Smith>',
               ':Jones')));

Example 1-22 Using the graphs Parameter

Example 1-22 uses the default graph to query the union of the <http://www.example.org/family/Smith> and <http://www.example.org/family/Jones> named graphs.

FROM TABLE(SEM_MATCH(
    '{?x :name ?name . ?x :email ?email }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null,null,null,
    SEM_GRAPHS('<http://www.example.org/family/Smith>', 
               ':Jones'),
    null));

Parent topic: Graph Patterns: Support for Curly Brace Syntax, and OPTIONAL, FILTER, UNION, and GRAPH Keywords

1.6.3 Graph Patterns: Support for SPARQL ASK Syntax

SEM_MATCH allows fully-specified SPARQL ASK queries in the query parameter.

ASK queries are used to test whether or not a solution exists for a given query pattern. In contrast to other forms of SPARQL queries, ASK queries do not return any information about solutions to the query pattern. Instead, such queries return "true"^^xsd:boolean if a solution exists and "false"^^xsd:boolean if no solution exists.

All SPARQL ASK queries return the same columns: ASK, ASK$RDFVID, ASK$_PREFIX, ASK$_SUFFIX, ASK$RDFVTYP, ASK$RDFCLOB, ASK$RDFLTYP, ASK$RDFLANG, SEM$ROWNUM. Note that these columns are the same as a SPARQL SELECT syntax query that projects a single ?ask variable.

SPARQL ASK queries will generally give better performance than an equivalent SPARQL SELECT syntax query because the ASK query does not have to retrieve lexical values for query variables, and query execution can stop after a single result has been found.

SPARQL ASK queries use the same syntax as SPARQL SELECT queries, but the topmost SELECT clause must be replaced with the keyword ASK.

Example 1-23 SPARQL ASK

Example 1-23 shows a SPARQL ASK query that determines whether or not any cameras are for sale with more than 10 megapixels that cost less than 50 dollars.

SELECT ask
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     ASK
     WHERE
      {?x :price ?p .
       ?x :megapixels ?m .
       FILTER (?p < 50 && ?m > 10)
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'), 
    null, null));

See also the W3C SPARQL specification for more information on SPARQL ASK queries, specifically: http://www.w3.org/TR/sparql11-query/#ask

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.4 Graph Patterns: Support for SPARQL CONSTRUCT Syntax

SEM_MATCH allows fully-specified SPARQL CONSTRUCT queries in the query parameter.

CONSTRUCT queries are used to build RDF graphs from stored RDF data. In contrast to SPARQL SELECT queries, CONSTRUCT queries return a set of RDF triples rather than a set of query solutions (variable bindings).

All SPARQL CONSTRUCT queries return the same columns from SEM_MATCH. These columns correspond to the subject, predicate and object of an RDF triple, and there are 10 columns for each triple component. In addition, a SEM$ROWNUM column is returned. More specifically, the following columns are returned:

SUBJ
SUBJ$RDFVID
SUBJ$_PREFIX
SUBJ$_SUFFIX
SUBJ$RDFVTYP
SUBJ$RDFCLOB
SUBJ$RDFLTYP
SUBJ$RDFLANG
SUBJ$RDFTERM
SUBJ$RDFCLBT
PRED
PRED$RDFVID
PRED$_PREFIX
PRED$_SUFFIX
PRED$RDFVTYP
PRED$RDFCLOB
PRED$RDFLTYP
PRED$RDFLANG
PRED$RDFTERM
PRED$RDFCLBT
OBJ
OBJ$RDFVID
OBJ$_PREFIX
OBJ$_SUFFIX
OBJ$RDFVTYP
OBJ$RDFCLOB
OBJ$RDFLTYP
OBJ$RDFLANG
OBJ$RDFTERM
OBJ$RDFCLBT
SEM$ROWNUM

For each component, COMP, COMP$RDFVID, COMP$_PREFIX, COMP$_SUFFIX, COMP$RDFVTYP, COMP$RDFCLOB, COMP$RDFLTYP, and COMP$RDFLANG correspond to the same values as those from SPARQL SELECT queries. COMP$RDFTERM holds a VARCHAR2(4000) RDF term in N-Triple syntax, and COMP$RDFCLBT holds a CLOB RDF term in N-Triple syntax.

SPARQL CONSTRUCT queries use the same syntax as SPARQL SELECT queries, except the topmost SELECT clause is replaced with a CONSTRUCT template. The CONSTRUCT template determines how to construct the result RDF graph using the results of the query pattern defined in the WHERE clause. A CONSTRUCT template consists of the keyword CONSTRUCT followed by sequence of SPARQL triple patterns that are enclosed within curly braces. The keywords OPTIONAL, UNION, FILTER, MINUS, BIND, VALUES, and GRAPH are not allowed within CONSTRUCT templates, and property path expressions are not allowed within CONSTRUCT templates. These keywords, however, are allowed within the query pattern inside the WHERE clause.

SPARQL CONSTRUCT queries build result RDF graphs in the following manner. For each result row returned by the WHERE clause, variable values are substituted into the CONSTRUCT template to create one or more RDF triples. Suppose the graph pattern in the WHERE clause of Example 1-24 returns the following result rows.

E$RDFTERM	FNAME$RDFTERM	LNAME$RDFTERM
ent:employee1	"Fred"	"Smith"
ent:employee2	"Jane"	"Brown"
ent:employee3	"Bill"	"Jones"

The overall SEM_MATCH CONSTRUCT query in Example 1-24 would then return the following rows, which correspond to six RDF triples (two for each result row of the query pattern).

SUBJ$RDFTERM	PRED$RDFTERM	OBJ$RDFTERM
ent:employee1	foaf:givenName	"Fred"
ent:employee1	foaf:familyName	"Smith"
ent:employee2	foaf:givenName	"Jane"
ent:employee2	foaf:familyName	"Brown"
ent:employee3	foaf:givenName	"Bill"
ent:employee3	foaf:familyName	"Jones"

There are two SEM_MATCH query options that influence the behavior of SPARQL CONSTRUCT: CONSTRUCT_UNIQUE=T and CONSTRUCT_STRICT=T. Using the CONSTRUCT_UNIQUE=T query option ensures that only unique RDF triples are returned from the CONSTRUCT query. Using the CONSTRUCT_STRICT=T query option ensures that only valid RDF triples are returned from the CONSTRUCT query. Valid RDF triples are those that have (1) a URI or blank node in the subject position, (2) a URI in the predicate position, and (3) a URI, blank node or RDF literal in the object position. Both of these query options are turned off by default for improved query performance.

Example 1-24 SPARQL CONSTRUCT

Example 1-24 shows a SPARQL CONSTRUCT query that builds an RDF graph of employee names using the foaf vocabulary.

SELECT subj$rdfterm, pred$rdfterm, obj$rdfterm
  FROM TABLE(SEM_MATCH(
    'PREFIX  ent: <http://www.example.org/enterprise/> 
     PREFIX foaf: <http://xmlns.com/foaf/0.1/>
     CONSTRUCT
      {?e foaf:givenName  ?fname .
       ?e foaf:familyName ?lname 
      }
     WHERE
      {?e ent:fname ?fname .
       ?e ent:lname ?lname 
      }',
    SEM_Models('enterprise'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-25 CONSTRUCT with Solution Modifiers

SPARQL SOLUTION modifiers can be used with CONSTRUCT queries. Example 1-25 shows the use of ORDER BY and LIMIT to build a graph about the top two highest-paid employees. Note that the LIMIT 2 clause applies to the query pattern not to the overall CONSTRUCT query. That is, the query pattern will return two result rows, but the overall CONSTRUCT query will return 6 RDF triples (three for each of the two employees bound to ?e).

SELECT subj$rdfterm, pred$rdfterm, obj$rdfterm
  FROM TABLE(SEM_MATCH(
    'PREFIX  ent: <http://www.example.org/enterprise/> 
     PREFIX foaf: <http://xmlns.com/foaf/0.1/>
     CONSTRUCT
      { ?e ent:fname       ?fname .
        ?e ent:lname       ?lname .
        ?e ent:dateOfBirth ?dob }
     WHERE
      { ?e ent:fname  ?fname .
        ?e ent:lname  ?lname .
        ?e ent:salary ?sal
      }
     ORDER BY DESC(?sal)
     LIMIT 2',
    SEM_Models('enterprise'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-26 SPARQL 1.1 Features with CONSTRUCT

SPARQL 1.1 features are supported within CONSTRUCT query patterns. Example 1-26 shows the use of subqueries and SELECT expressions within a CONSTRUCT query.

SELECT subj$rdfterm, pred$rdfterm, obj$rdfterm
  FROM TABLE(SEM_MATCH(
    'PREFIX  ent: <http://www.example.org/enterprise/> 
     PREFIX foaf: <http://xmlns.com/foaf/0.1/>
     CONSTRUCT
      { ?e foaf:name  ?name }
     WHERE
      { SELECT ?e (CONCAT(?fname," ",?lname) AS ?name)
        WHERE { ?e ent:fname ?fname .
                ?e ent:lname ?lname }
      }',
    SEM_Models('enterprise'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-27 SPARQL CONSTRUCT with Named Graphs

Named graph data cannot be returned from SPARQL CONSTRUCT queries because, in accordance with the W3C SPARQL specification, only RDF triples are returned, not RDF quads. The FROM, FROM NAMED and GRAPH keywords, however, can be used when matching the query pattern defined in the WHERE clause.

Example 1-27 constructs an RDF graph with ent:name triples from the UNION of named graphs ent:g1 and ent:g2, ent:dateOfBirth triples from named graph ent:g3, and ent:ssn triples from named graph ent:g4.

SELECT subj$rdfterm, pred$rdfterm, obj$rdfterm
  FROM TABLE(SEM_MATCH(
    'PREFIX  ent: <http://www.example.org/enterprise/> 
     PREFIX foaf: <http://xmlns.com/foaf/0.1/>
     CONSTRUCT
      { ?e ent:name ?name .
        ?e ent:dateOfBirth ?dob .
        ?e ent:ssn ?ssn
      }
     FROM ent:g1
     FROM ent:g2
     FROM NAMED ent:g3
     FROM NAMED ent:g4
     WHERE
      { ?e foaf:name ?name .
        GRAPH ent:g3 { ?e ent:dateOfBirth ?dob }
        GRAPH ent:g4 { ?e ent:ssn ?ssn } 
      }',
    SEM_Models('enterprise'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-28 SPARQL CONSTRUCT Normal Form

SELECT subj$rdfterm, pred$rdfterm, obj$rdfterm
  FROM TABLE(SEM_MATCH(
    'PREFIX  ent: <http://www.example.org/enterprise/> 
     PREFIX foaf: <http://xmlns.com/foaf/0.1/>
     CONSTRUCT
      {?e foaf:givenName  ?fname .
       ?e foaf:familyName ?lname 
      }
     WHERE
      {?e ent:fname ?fname .
       ?e ent:lname ?lname 
      }',
    SEM_Models('enterprise'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-29 SPARQL CONSTRUCT Short Form

A short form of CONSTRUCT is supported when the CONSTRUCT template is exactly the same as the WHERE clause. In this case, only the keyword CONSTRUCT is needed, and the graph pattern in the WHERE clause will also be used as a CONSTRUCT template. Example 1-29 shows the short form of Example 1-28.

SELECT subj$rdfterm, pred$rdfterm, obj$rdfterm
  FROM TABLE(SEM_MATCH(
    'PREFIX  ent: <http://www.example.org/enterprise/> 
     PREFIX foaf: <http://xmlns.com/foaf/0.1/>
     CONSTRUCT
     WHERE
      {?e ent:fname ?fname .
       ?e ent:lname ?lname 
      }',
    SEM_Models('enterprise'),
    SEM_Rulebases('RDFS'),
    null, null));

Typical SPARQL CONSTRUCT Workflow

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.4.1 Typical SPARQL CONSTRUCT Workflow

A typical workflow for SPARQL CONSTRUCT would be to execute a CONSTRUCT query to extract and/or transform RDF triple data from an existing semantic model and then load this data into an existing or new semantic model. The data loading can be accomplished through simple INSERT statements or executing the SEM_APIS.BULK_LOAD_FROM_STAGING_TABLE procedure.

Example 1-30 SPARQL CONSTRUCT Workflow

Example 1-30 constructs foaf:name triples from existing ent:fname and ent:lname triples and then bulk loads these new triples back into the original model. Afterward, you can query the original model for foaf:name values.

-- use create table as select to build a staging table 
CREATE TABLE STAB(RDF$STC_sub, RDF$STC_pred, RDF$STC_obj) AS
SELECT subj$rdfterm, pred$rdfterm, obj$rdfterm
FROM TABLE(SEM_MATCH(
 'PREFIX  ent: <http://www.example.org/enterprise/> 
  PREFIX foaf: <http://xmlns.com/foaf/0.1/>
  CONSTRUCT
   { ?e foaf:name  ?name }
  WHERE
   { SELECT ?e (CONCAT(?fname," ",?lname) AS ?name)
     WHERE { ?e ent:fname ?fname .
             ?e ent:lname ?lname }
   }',
 SEM_Models('enterprise'),
 null, null, null)); 
 
-- grant privileges on STAB
GRANT SELECT ON STAB TO MDSYS;
 
-- bulk load data back into the enterprise model
BEGIN
  SEM_APIS.BULK_LOAD_FROM_STAGING_TABLE(
    model_name=>'enterprise',
    table_owner=>'rdfuser',
    table_name=>'stab',
    flags=>' parallel_create_index parallel=4 ');
END;
/
 
-- query for foaf:name data
SELECT e$rdfterm, name$rdfterm
FROM TABLE(SEM_MATCH(
 'PREFIX foaf: <http://xmlns.com/foaf/0.1/>
  SELECT ?e ?name
  WHERE { ?e foaf:name ?name }',
 SEM_Models('enterprise'),
null, null, null));

See also the W3C SPARQL specification for more information on SPARQL CONSTRUCT queries, specifically: http://www.w3.org/TR/sparql11-query/#construct

Parent topic: Graph Patterns: Support for SPARQL CONSTRUCT Syntax

1.6.5 Graph Patterns: Support for SPARQL DESCRIBE Syntax

SEM_MATCH allows fully-specified SPARQL DESCRIBE queries in the query parameter.

SPARQL DESCRIBE queries are useful for exploring RDF data sets. You can easily find information about a given resource or set of resources without knowing information about the exact RDF properties used in the data set. A DESCRIBE query returns a "description" of a resource r, where a "description" is the set of RDF triples in the query data set that contain r in either the subject or object position.

Like CONSTRUCT queries, DESCRIBE queries return an RDF graph instead of result bindings. Each DESCRIBE query, therefore, returns the same columns as a CONSTRUCT query (see Graph Patterns: Support for SPARQL CONSTRUCT Syntax for a listing of return columns).

SPARQL DESCRIBE queries use the same syntax as SPARQL SELECT queries, except the topmost SELECT clause is replaced with a DESCRIBE clause. A DESCRIBE clause consists of the DESCRIBE keyword followed by a sequence of URIs and/or variables separated by whitespace or the DESCRIBE keyword followed by a single * (asterisk).

Two SEM_MATCH query options affect SPARQL DESCRIBE queries: CONSTRUCT_UNIQUE=T and CONSTRUCT_STRICT=T. CONSTRUCT_UNIQUE=T ensures that duplicate triples are eliminated from the result, and CONSTRUCT_STRICT=T ensures that invalid triples are eliminated from the result. Both of these options are turned off by default. These options are described in more detail in Graph Patterns: Support for SPARQL CONSTRUCT Syntax.

See also the W3C SPARQL specification for more information on SPARQL DESCRIBE queries, specifically: http://www.w3.org/TR/sparql11-query/#describe

Example 1-31 SPARQL DESCRIBE Short Form

A short form of SPARQL DESCRIBE is provided to describe a single constant URI. In the short form, only a DESCRIBE clause is needed. Example 1-31 shows a short form SPARQL DESCRIBE query.

SELECT subj$rdfterm, pred$rdfterm, obj$rdfterm
FROM TABLE(SEM_MATCH(
 'DESCRIBE <http://www.example.org/enterprise/emp_1>',
 SEM_Models('enterprise'),
null, null, null));

Example 1-32 SPARQL DESCRIBE Normal Form

The normal form of SPARQL DESCRIBE specifies a DESCRIBE clause and a SPARQL query pattern, possibly including solution modifiers. Example 1-32 shows a SPARQL DESCRIBE query that describes all employees whose departments are located in New Hampshire.

SELECT subj$rdfterm, pred$rdfterm, obj$rdfterm
FROM TABLE(SEM_MATCH(
 'PREFIX  ent: <http://www.example.org/enterprise/>
  DESCRIBE ?e
  WHERE 
   { ?e ent:department ?dept .
     ?dept ent:locatedIn "New Hampshire" }',
 SEM_Models('enterprise'),
null, null, null));

Example 1-33 DESCRIBE *

With the normal form of DESCRIBE, as shown in Example 1-32, all resources bound to variables listed in the DESCRIBE clause are described. In Example 1-32, all employees returned from the query pattern and bound to ?e will be described. When DESCRIBE * is used, all visible variables in the query are described.

Example 1-33 shows a modified version of Example 1-32 that describes both employees (bound to ?e) and departments (bound to ?dept).

SELECT subj$rdfterm, pred$rdfterm, obj$rdfterm
FROM TABLE(SEM_MATCH(
 'PREFIX  ent: <http://www.example.org/enterprise/>
  DESCRIBE *
  WHERE 
   { ?e ent:department ?dept .
     ?dept ent:locatedIn "New Hampshire" }',
 SEM_Models('enterprise'),
null, null, null));

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.6 Graph Patterns: Support for SPARQL SELECT Syntax

In addition to curly-brace graph patterns, SEM_MATCH allows fully-specified SPARQL SELECT queries in the query parameter. When using the SPARQL SELECT syntax option, SEM_MATCH supports the following query constructs: BASE, PREFIX, SELECT, SELECT DISTINCT, FROM, FROM NAMED, WHERE, ORDER BY, LIMIT, and OFFSET. Each SPARQL SELECT syntax query must include a SELECT clause and a graph pattern.

A key difference between curly-brace and SPARQL SELECT syntax when using SEM_MATCH is that only variables appearing in the SPARQL SELECT clause are returned from SEM_MATCH when using SPARQL SELECT syntax.

One additional column, SEM$ROWNUM, is returned from SEM_MATCH when using SPARQL SELECT syntax. This NUMBER column can be used to order the results of a SEM_MATCH query so that the result order matches the ordering specified by a SPARQL ORDER BY clause.

The SPARQL ORDER BY clause can be used to order the results of SEM_MATCH queries. This clause specifies a sequence of comparators used to order the results of a given query. A comparator consists of an expression composed of variables, RDF terms, arithmetic operators (+, -, *, /), Boolean operators and logical connectives (||, &&, !), comparison operators (<, >, <=, >=, =, !=), and any functions available for use in FILTER expressions.

The following order of operations is used when evaluating SPARQL SELECT queries:

Graph pattern matching
Grouping (see Grouping and Aggregation.)
Aggregates (see Grouping and Aggregation)
Having (see Grouping and Aggregation)
Values (see Value Assignment)
Select expressions
Order by
Projection
Distinct
Offset
Limit

See also the W3C SPARQL specification for more information on SPARQL BASE, PREFIX, SELECT, SELECT DISTINCT, FROM, FROM NAMED, WHERE, ORDER BY, LIMIT, and OFFSET constructs, specifically: http://www.w3.org/TR/sparql11-query/

Example 1-34 SPARQL PREFIX, SELECT, and WHERE Clauses

Example 1-34 uses the following SPARQL constructs:

SPARQL PREFIX clause to specify an abbreviation for the http://www.example.org/family/ and http://xmlns.com/foaf/0.1/ namespaces
SPARQL SELECT clause to specify the set of variables to project out of the query
SPARQL WHERE clause to specify the query graph pattern

SELECT y, name
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/family/> 
     PREFIX foaf: <http://xmlns.com/foaf/0.1/>
     SELECT ?y ?name
     WHERE
     {?x :grandParentOf ?y . 
      ?x foaf:name ?name }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    null, null));

Example 1-34 returns the following columns: y, y$RDFVID, y$_PREFIX, y$_SUFFIX, y$RDFVTYP, y$RDFCLOB, y$RDFLTYP, y$RDFLANG, name, name$RDFVID, name$_PREFIX, name$_SUFFIX, name$RDFVTYP, name$RDFCLOB, name$RDFLTYP, name$RDFLANG, and SEM$ROWNUM.

Example 1-35 SPARQL SELECT * (All Variables in Triple Pattern)

The SPARQL SELECT clause specifies either (A) a sequence of variables and/or expressions (see Expressions in the SELECT Clause), or (B) * (asterisk), which projects all variables that appear in a specified triple pattern. Example 1-35 uses the SPARQL SELECT clause to select all variables that appear in a specified triple pattern.

SELECT x, y, name
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/family/> 
     PREFIX foaf: <http://xmlns.com/foaf/0.1/>
     SELECT *
     WHERE
     {?x :grandParentOf ?y . 
      ?x foaf:name ?name }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    null, null));

Example 1-36 SPARQL SELECT DISTINCT

The DISTINCT keyword can be used after SELECT to remove duplicate result rows. Example 1-36 uses SELECT DISTINCT to select only the distinct names.

SELECT name
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/family/> 
     PREFIX foaf: <http://xmlns.com/foaf/0.1/>
     SELECT DISTINCT ?name
     WHERE
     {?x :grandParentOf ?y . 
      ?x foaf:name ?name }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    null, null));

Example 1-37 RDF Dataset Specification Using FROM and FROM NAMED

SPARQL FROM and FROM NAMED are used to specify the RDF dataset for a query. FROM clauses are used to specify the set of graphs that make up the default graph, and FROM NAMED clauses are used to specify the set of graphs that make up the set of named graphs. Example 1-37 uses FROM and FROM NAMED to select email addresses and friend of relationships from the union of the <http://www.friends.com/friends> and <http://www.contacts.com/contacts> graphs and grandparent information from the <http://www.example.org/family/Smith> and <http://www.example.org/family/Jones> graphs.

SELECT x, y, z, email
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/family/> 
     PREFIX foaf: <http://xmlns.com/foaf/0.1/>
     PREFIX friends: <http://www.friends.com/>
     PREFIX contacts: <http://www.contacts.com/>
     SELECT *
     FROM friends:friends
     FROM contacts:contacts
     FROM NAMED :Smith
     FROM NAMED :Jones
     WHERE
     {?x foaf:frendOf ?y .
      ?x :email ?email .
      GRAPH ?g {
        ?x :grandParentOf ?z }
     }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    null, null));

Example 1-38 SPARQL ORDER BY

In a SPARQL ORDER BY clause:

Single variable ordering conditions do not require enclosing parenthesis, but parentheses are required for more complex ordering conditions.
An optional ASC() or DESC() order modifier can be used to indicate the desired order (ascending or descending, respectively). Ascending is the default order.
When using SPARQL ORDER BY in SEM_MATCH, the containing SQL query should be ordered by SEM$ROWNUM to ensure that the desired ordering is maintained through any enclosing SQL blocks.

Example 1-38 uses a SPARQL ORDER BY clause to select all cameras, and it specifies ordering by descending type and ascending total price (price * (1 - discount) * (1 + tax)).

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT * 
     WHERE
      {?x :price ?p .
       ?x :discount ?d .
       ?x :tax ?t .
       ?x :cameraType ?cType .
      }
     ORDER BY DESC(?cType) ASC(?p * (1-?d) * (1+?t))',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'), 
    null, null))
ORDER BY SEM$ROWNUM;

Example 1-39 SPARQL LIMIT

SPARQL LIMIT and SPARQL OFFSET can be used to select different subsets of the query solutions. Example 1-39 uses SPARQL LIMIT to select the five cheapest cameras, and Example 1-40 uses SPARQL LIMIT and OFFSET to select the fifth through tenth cheapest cameras.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ?cType ?p
     WHERE
      {?x :price ?p .
       ?x :cameraType ?cType .
      }
     ORDER BY ASC(?p)
     LIMIT 5',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'), 
    null, null))
ORDER BY SEM$ROWNUM;

Example 1-40 SPARQL OFFSET

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ?cType ?p
     WHERE
      {?x :price ?p .
       ?x :cameraType ?cType .
      }
     ORDER BY ASC(?p)
     LIMIT 5
     OFFSET 5',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'), 
    null, null))
ORDER BY SEM$ROWNUM;

Example 1-41 Query Using Full URIs

The SPARQL BASE keyword is used to set a global prefix. All relative IRIs will be resolved with the BASE IRI using the basic algorithm described in Section 5.2 of the Uniform Resource Identifier (URI): Generic Syntax (RFC3986) (http://www.ietf.org/rfc/rfc3986.txt). Example 1-41 is a simple query using full URIs, and Example 1-42 is an equivalent query using a base IRI.

SELECT *
  FROM TABLE(SEM_MATCH(
    'SELECT ?employee ?position
     WHERE
      {?x <http://www.example.org/employee> ?p .
       ?p <http://www.example.org/employee/name> ?employee .
       ?p <http://www.example.org/employee/position> ?pos .
       ?pos <http://www.example.org/positions/name> ?position
      }',
    SEM_Models('enterprise'),
    null, 
    null, null))
ORDER BY 1,2;

Example 1-42 Query Using a Base IRI

SELECT *
  FROM TABLE(SEM_MATCH(
    'BASE <http://www.example.org/>
     SELECT ?employee ?position
     WHERE
      {?x <employee> ?p .
       ?p <employee/name> ?employee .
       ?p <employee/position> ?pos .
       ?pos <positions/name> ?position
      }',
    SEM_Models('enterprise'),
    null, 
    null, null))
ORDER BY 1,2;

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.7 Graph Patterns: Support for SPARQL 1.1 Constructs

SEM_MATCH supports the following SPARQL 1.1 constructs:

An expanded set of functions (all items in Table 1-12 in Graph Patterns: Support for Curly Brace Syntax_ and OPTIONAL_ FILTER_ UNION_ and GRAPH Keywords)

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.7.1 Expressions in the SELECT Clause

Expressions can be used in the SELECT clause to project the value of an expression from a query. A SELECT expression is composed of variables, RDF terms, arithmetic operators (+, -, *, /), Boolean operators and logical connectives (||, &&, !), comparison operators (<, >, <=, >=, =, !=), and any functions available for use in FILTER expressions. The expression must be aliased to a single variable using the AS keyword, and the overall <expression> AS <alias> fragment must be enclosed in parentheses. The alias variable cannot already be defined in the query. A SELECT expression may reference the result of a previous SELECT expression (that is, an expression that appears earlier in the SELECT clause).

Example 1-43 SPARQL SELECT Expression

Example 1-43 uses a SELECT expression to project the total price for each camera.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ((?p * (1-?d) * (1+?t)) AS ?totalPrice) 
     WHERE
      {?x :price ?p .
       ?x :discount ?d .
       ?x :tax ?t .
       ?x :cameraType ?cType .
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'), 
    null, null));

Example 1-44 SPARQL SELECT Expressions (2)

Example 1-44 uses two SELECT expressions to project the discount price with and without sales tax.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ((?p * (1-?d)) AS ?preTaxPrice) ((?preTaxPrice * (1+?t)) AS ?finalPrice)
     WHERE
      {?x :price ?p .
       ?x :discount ?d .
       ?x :tax ?t .
       ?x :cameraType ?cType .
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'), 
    null, null));

Parent topic: Graph Patterns: Support for SPARQL 1.1 Constructs

1.6.7.2 Subqueries

Subqueries are allowed with SPARQL SELECT syntax. That is, fully-specified SPARQL SELECT queries may be embedded within other SPARQL SELECT queries. Subqueries have many uses, for example, limiting the number of results from a subcomponent of a query.

Example 1-45 SPARQL SELECT Subquery

Example 1-45 uses a subquery to find the manufacturer that makes the cheapest camera and then finds all other cameras made by this manufacturer.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?c1
     WHERE {?c1 rdf:type :Camera .
            ?c1 :manufacturer ?m .
            {
             SELECT ?m
             WHERE {?c2 rdf:Type :Camera .
                    ?c2 :price ?p .
                    ?c2 :manufacturer ?m .
             }
             ORDER BY ASC(?p)
             LIMIT 1
            }
     }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'), 
    null, null));

Subqueries are logically evaluated first, and the results are projected up to the outer query. Note that only variables projected in the subquery's SELECT clause are visible to the outer query.

Parent topic: Graph Patterns: Support for SPARQL 1.1 Constructs

1.6.7.3 Grouping and Aggregation

The GROUP BY keyword used to perform grouping. Syntactically, the GROUP BY keyword must appear after the WHERE clause and before any solution modifiers such as ORDER BY or LIMIT.

Aggregates are used to compute values across results within a group. An aggregate operates over a collection of values and produces a single value as a result. SEM_MATCH supports the following built-in Aggregates: COUNT, SUM, MIN, MAX, AVG, GROUP_CONCAT and SAMPLE. These aggregates are described in Table 1-15.

Table 1-15 Built-in Aggregates

Aggregate	Description
AVG(expression)	Returns the numeric average of expression over the values within a group.
COUNT(* \| expression)	Counts the number of times expression has a bound, non-error value within a group; asterisk (*) counts the number of results within a group.
GROUP_CONCAT(expression [; SEPARATOR = "STRING"])	Performs string concatenation of expression over the values within a group. If provided, an optional separator string will be placed between each value.
MAX(expression)	Returns the maximum value of expression within a group based on the ordering defined by SPARQL ORDER BY.
MIN(expression)	Returns the minimum value of expression within a group based on the ordering defined by SPARQL ORDER BY.
SAMPLE(expression)	Returns expression evaluated for a single arbitrary value from a group.
SUM(expression)	Calculates the numeric sum of expression over the values within a group.

Certain restrictions on variable references apply when using grouping and aggregation. Only group-by variables (single variables in the GROUP BY clause) and alias variables from GROUP BY value assignments can be used in non-aggregate expressions in the SELECT or HAVING clauses.

Example 1-46 Simple Grouping Query

Example 1-46 shows a query that uses the GROUP BY keyword to find all the different types of cameras.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?cType
     WHERE
      {?x rdf:type :Camera .
       ?x :cameraType ?cType .
      }
     GROUP BY ?cType',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'), 
    null, null));

A grouping query partitions the query results into a collection of groups based on a grouping expression (?cType in Example 1-46) such that each result within a group has the same values for the grouping expression. The final result of the grouping operation will include one row for each group.

Example 1-47 Complex Grouping Expression

A grouping expression consists of a sequence of one or more of the following: a variable, an expression, or a value assignment of the form (<expression> as <alias>). Example 1-47 shows a grouping query that uses one of each type of component in the grouping expression.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?cType ?totalPrice
     WHERE
      {?x rdf:type :Camera .
       ?x :cameraType ?cType .
       ?x :manufacturer ?m .
       ?x :price ?p .
       ?x :tax ?t .
      }
     GROUP BY ?cType (STR(?m)) ((?p*(1+?t)) AS ?totalPrice)',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-48 Aggregation

Example 1-48 uses aggregates to select the maximum, minimum, and average price for each type of camera.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?cType 
            (MAX(?p) AS ?maxPrice) 
            (MIN(?p) AS ?minPrice) 
            (AVG(?p) AS ?avgPrice)
     WHERE
      {?x rdf:type :Camera .
       ?x :cameraType ?cType .
       ?x :manufacturer ?m .
       ?x :price ?p .
      }
     GROUP BY ?cType',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-49 Aggregation Without Grouping

If an aggregate is used without a grouping expression, then the entire result set is treated as a single group. Example 1-49 computes the total number of cameras for the whole data set.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT (COUNT(?x) as ?cameraCnt)
     WHERE
      { ?x rdf:type :Camera 
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-50 Aggregation with DISTINCT

The DISTINCT keyword can optionally be used as a modifier for each aggregate. When DISTINCT is used, duplicate values are removed from each group before computing the aggregate. Syntactically, DISTINCT must appear as the first argument to the aggregate. Example 1-50 uses DISTINCT to find the number of distinct camera manufacturers. In this case, duplicate values of STR(?m) are removed before counting.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT (COUNT(DISTINCT STR(?m)) as ?mCnt)
     WHERE
      { ?x rdf:type :Camera .
        ?x :manufacturer ?m
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-51 HAVING Clause

The HAVING keyword can be used to filter groups based on constraints. HAVING expressions can be composed of variables, RDF terms, arithmetic operators (+, -, *, /), Boolean operators and logical connectives (||, &&, !), comparison operators (<, >, <=, >=, =, !=), aggregates, and any functions available for use in FILTER expressions. Syntactically, the HAVING keyword appears after the GROUP BY clause and before any other solution modifiers such as ORDER BY or LIMIT.

Example 1-51 uses a HAVING expression to find all manufacturers that sell cameras for less than $200.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?m
     WHERE
      { ?x rdf:type :Camera .
        ?x :manufacturer ?m .
        ?x :price ?p
      }
     GROUP BY ?m
     HAVING (MIN(?p) < 200)
     ORDER BY ASC(?m)',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Parent topic: Graph Patterns: Support for SPARQL 1.1 Constructs

1.6.7.4 Negation

SEM_MATCH supports two forms of negation in SPARQL query patterns: NOT EXISTS and MINUS. NOT EXISTS can be used to filter results based on whether or not a graph pattern matches, and MINUS can be used to remove solutions based on their relation to another graph pattern.

Example 1-52 Negation with NOT EXISTS

Example 1-52 uses a NOT EXISTS FILTER to select those cameras that do not have any user reviews.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ?cType ?p
     WHERE
      {?x :price ?p .
       ?x :cameraType ?cType .
       FILTER( NOT EXISTS({?x :userReview ?r}) )
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'), 
    null, null));

Example 1-53 EXISTS

Conversely, the EXISTS operator can be used to ensure that a pattern matches. Example 1-53 uses an EXISTS FILTER to select only those cameras that have a user review.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ?cType ?p
     WHERE
      {?x :price ?p .
       ?x :cameraType ?cType .
       FILTER( EXISTS({?x :userReview ?r}) )
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'), 
    null, null));

Example 1-54 Negation with MINUS

Example 1-54 uses MINUS to arrive at the same result as Example 1-52. Only those solutions that are not compatible with solutions from the MINUS pattern are included in the result. That is, if a solution has the same values for all shared variables as a solution from the MINUS pattern, it is removed from the result.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ?cType ?p
     WHERE
      {?x :price ?p .
       ?x :cameraType ?cType .
       MINUS {?x :userReview ?r}
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'), 
    null, null));

Example 1-55 Negation with NOT EXISTS (2)

NOT EXISTS and MINUS represent two different styles of negation and have different results in certain cases. One such case occurs when no variables are shared between the negation pattern and the rest of the query. For example, the NOT EXISTS query in Example 1-55 removes all solutions because {?subj ?prop ?obj} matches any triple, but the MINUS query in Example 1-56 removes no solutions because there are no shared variables.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ?cType ?p
     WHERE
      {?x :price ?p .
       ?x :cameraType ?cType .
       FILTER( NOT EXISTS({?subj ?prop ?obj}) )
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-56 Negation with MINUS (2)

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ?cType ?p
     WHERE
      {?x :price ?p .
       ?x :cameraType ?cType .
       MINUS {?subj ?prop ?obj}
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Parent topic: Graph Patterns: Support for SPARQL 1.1 Constructs

1.6.7.5 Value Assignment

SEM_MATCH provides a variety of ways to assign values to variables in a SPARQL query.

The value of an expression can be assigned to a new variable in three ways: (1) expressions in the SELECT clause, (2) expressions in the GROUP BY clause, and (3) the BIND keyword. In each case, the new variable must not already be defined in the query. After assignment, the new variable can be used in the query and returned in results. As discussed in Expressions in the SELECT Clause, the syntax for value assignment is (<expression> AS <alias>) where alias is the new variable, for example, ((?price * (1+?tax)) AS ?totalPrice).

Example 1-57 Nested SELECT Expression

Example 1-57 uses a nested SELECT expression to compute the total price of a camera and assign the value to a variable (?totalPrice). This variable is then used in a FILTER in the outer query to find cameras costing less than $200.

SELECT *
 FROM TABLE(SEM_MATCH(
   'PREFIX : <http://www.example.org/electronics/> 
    SELECT ?x ?cType ?totalPrice
    WHERE
     {?x :cameraType ?cType .
       { SELECT ?x ( ((?price*(1+?tax)) AS ?totalPrice )
         WHERE { ?x :price ?price .
                 ?x :tax ?tax }
       }
      FILTER (?totalPrice < 200)
     }',
   SEM_Models('electronics'),
   SEM_Rulebases('RDFS'),
   null, null));

Example 1-58 BIND

The BIND keyword can be used inside a basic graph pattern to assign a value and is syntactically more compact than an equivalent nested SELECT expression. Example 1-58 uses the BIND keyword to expresses a query that is logically equivalent to Example 1-57.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ?cType ?totalPrice
     WHERE
      {?x :cameraType ?cType .
       ?x :price ?price .
       ?x :tax ?tax .
       BIND ( ((?price*(1+?tax)) AS ?totalPrice )
       FILTER (?totalPrice < 200)
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-59 GROUP BY Expression

Value assignments in the GROUP BY clause can subsequently be used in the SELECT clause, the HAVING clause, and the outer query (in the case of a nested grouping query). Example 1-59 uses a GROUP BY expression to find the maximum number of megapixels for cameras at each price point less than $1000.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?totalPrice (MAX(?mp) as ?maxMP)
     WHERE
      {?x rdf:type :Camera .
       ?x :price ?price .
       ?x :tax ?tax .
       GROUP BY ( ((?price*(1+?tax)) AS ?totalPrice )
       HAVING (?totalPrice < 1000)
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-60 VALUES

In addition to the preceding three ways to assign the value of an expression to a new variable, the VALUES keyword can be used to introduce an unordered solution sequence that is combined with the query results through a join operation. A VALUES block can appear inside a query pattern or at the end of a SPARQL SELECT query block after any solution modifiers. The VALUES construct can be used in subqueries.

Example 1-60 uses the VALUES keyword to constrain the query results to DSLR cameras made by :Company1 or any type of camera made by :Company2. The keyword UNDEF is used to represent an unbound variable in the solution sequence.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ?cType ?m
     WHERE 
      { ?x rdf:type :Camera .
        ?x :cameraType ?cType .
        ?x :manufacturer ?m
      }
     VALUES (?cType ?m)
     { ("DSLR" :Company1)
       (UNDEF  :Company2) 
     }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-61 Simplified VALUES Syntax

A simplified syntax can be used for the common case of a single variable. Specifically, the parentheses around the variable and each solution can be omitted. Example 1-61 uses the simplified syntax to constrain the query results to cameras made by :Company1 or :Company2.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ?cType ?m
     WHERE 
      { ?x rdf:type :Camera .
        ?x :cameraType ?cType .
        ?x :manufacturer ?m
      }
     VALUES ?m
     { :Company1
       :Company2 
     }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Example 1-62 Inline VALUES Block

Example 1-62 also constrains the query results to any camera made by :Company1 or :Company2, but specifies the VALUES block inside the query pattern.

SELECT *
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?x ?cType ?m
     WHERE 
      { VALUES ?m { :Company1 :Company2 }
        ?x rdf:type :Camera .
        ?x :cameraType ?cType .
        ?x :manufacturer ?m
      }',
    SEM_Models('electronics'),
    SEM_Rulebases('RDFS'),
    null, null));

Parent topic: Graph Patterns: Support for SPARQL 1.1 Constructs

1.6.7.6 Property Paths

A SPARQL Property Path describes a possible path between two RDF resources (nodes) in an RDF graph. A property path appears in the predicate position of a triple pattern and uses a regular expression-like syntax to place constraints on the properties (edges) making up a path from the subject of the triple pattern to the object of a triple pattern. Property paths allow SPARQL queries to match arbitrary length paths in the RDF graph and also provide a more concise way to express other graph patterns.

Table 1-16 describes the syntax constructs available for constructing SPARQL Property Paths. Note that iri is either an IRI or a prefixed name, and elt is a property path element, which may itself be composed of other property path elements.

Table 1-16 Property Path Syntax Constructs

Syntax Construct	Matches
iri	An IRI or a prefixed name. A path of length 1 (one).
^elt	Inverse path (object to subject).
!iri or !(iri1 \| … \| irin)	Negated property set. An IRI that is not one of irii.
!^iri or !(iri1 \| … \| irij \| ^irij+1 \| … \| ^irin)	Negated property set with some inverse properties. An IRI that is not one of irii, nor one of irij+1...irin as reverse paths. !^iri is short for !(^iri). The order of properties and inverse properties is not important. They can occur in mixed order.
(elt)	A group path elt; brackets control precedence.
elt₁ / elt₂	A sequence path of elt₁, followed by elt₂.
elt₁ \| elt₂	An alternative path of elt₁, or elt₂ (all possibilities are tried).
elt*	A path of zero or more occurrences of elt.
elt+	A path of one or more occurrences of elt.
elt?	A path of zero or one occurrence of elt.

The precedence of the syntax constructs is as follows (from highest to lowest):

IRI, prefixed names
Negated property sets
Groups
Unary operators *, ?, +
Unary ^ inverse links
Binary operator /
Binary operator |

Precedence is left-to-right within groups.

Special Considerations for Property Path Operators + and *

In general, truly unbounded graph traversals using the + (plus sign) and * (asterisk) operator can be very expensive. For this reason, a depth-limited version of the + and * operator is used by default, and the default depth limit is 10. In addition, the depth-limited implementation can be run in parallel. The ALL_MAX_PP_DEPTH(n) SEM_MATCH query option or the MAX_PP_DEPTH(n) inline HINT0 query optimizer hint can be used to change the depth-limit setting. To achieve a truly unbounded traversal, you can set a depth limit of less than 1 to fall back to a CONNECT BY-based implementation.

Query Hints for Property Paths

Other query hints are available to influence the performance of property path queries. The ALLOW_PP_DUP=T query option can be used with * and + queries to allow duplicate results. Allowing duplicate results may return the first rows from a query faster. In addition, ALL_USE_PP_HASH and ALL_USE_PP_NL query options are available to influence the join types used when evaluating property path expressions. Analogous USE_PP_HASH and USE_PP_NL inline HINT0 query optimizer hints can also be used.

Example 1-63 SPARQL Property Path (Using rdfs:subClassOf Relations)

Example 1-63 uses a property path to find all Males based on transitivity of the rdfs:subClassOf relationship. A property path allows matching an arbitrary number of consecutive rdfs:subClassOf relations.

SELECT x, name
  FROM TABLE(SEM_MATCH(
    '{ ?x foaf:name ?name .
       ?x rdf:type ?t .
       ?t rdfs:subClassOf* :Male }',
    SEM_Models('family'),
    null, 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/') 
                SEM_ALIAS('foaf',' http://xmlns.com/foaf/0.1/')),
    null));

Example 1-64 SPARQL Property Path (Using foaf:friendOf or foaf:knows Relationships)

Example 1-64 uses a property path to find all of Scott's close friends (those people reachable within two hops using foaf:friendOf or foaf:knows relationships).

SELECT name
  FROM TABLE(SEM_MATCH(
    '{ { :Scott (foaf:friendOf | foaf:knows) ?f }
       UNION
       { :Scott (foaf:friendOf | foaf:knows)/(foaf:friendOf | foaf:knows) ?f }
       ?f foaf:name ?name .
       FILTER (!sameTerm(?f, :Scott)) }',
    SEM_Models('family'),
    null, 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/'),
                SEM_ALIAS('foaf',' http://xmlns.com/foaf/0.1/')),
    null));

Example 1-65 Specifying Property Path Maximum Depth Value

Example 1-65 specifies a maximum depth of 12 for all property path expressions with the ALL_MAX_PP_DEPTH(n) query option value.

SELECT x, name
  FROM TABLE(SEM_MATCH(
    '{ ?x foaf:name ?name .
       ?x rdf:type ?t .
       ?t rdfs:subClassOf* :Male }',
    SEM_Models('family'),
    null, 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/') 
                SEM_ALIAS('foaf',' http://xmlns.com/foaf/0.1/')),
    null,
    null,
    ' ALL_MAX_PP_DEPTH(12) '));

Example 1-66 Specifying Property Path Join Hint

Example 1-66 shows an inline HINT0 query optimizer hint that requests a nested loop join for evaluating the property path expression.

SELECT x, name
  FROM TABLE(SEM_MATCH(
    '{ # HINT0={ USE_PP_NL }
       ?x foaf:name ?name .
       ?x rdf:type ?t .
       ?t rdfs:subClassOf* :Male }',
    SEM_Models('family'),
    null, 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/') 
                SEM_ALIAS('foaf',' http://xmlns.com/foaf/0.1/')),
    null));

Parent topic: Graph Patterns: Support for SPARQL 1.1 Constructs

1.6.8 Graph Patterns: Support for SPARQL 1.1 Federated Query

SEM_MATCH supports SPARQL 1.1 Federated Query (see http://www.w3.org/TR/sparql11-federated-query/#SPROT). The SERVICE construct can be used to retrieve results from a specified SPARQL endpoint URL. With this capability, you can combine local RDF data (native RDF data or RDF views of relational data) with other, possibly remote, RDF data served by a W3C standards-compliant SPARQL endpoint.

Example 1-67 SPARQL SERVICE Clause to Retrieve All Triples

Example 1-67 shows a query that uses a SERVICE clause to retrieve all triples from the SPARQL endpoint available at http://www.example1.org/sparql.

SELECT s, p, o
  FROM TABLE(SEM_MATCH(
    'SELECT ?s ?p ?o
     WHERE {
       SERVICE <http://www.example1.org/sparql>{ ?s ?p ?o }
     }',
    SEM_Models('electronics'),
    null, null, null, null, ' '));

Example 1-68 SPARQL SERVICE Clause to Join Remote and Local RDF Data

Example 1-68 joins remote RDF data with local RDF data. This example joins camera types ?cType from local model electronics with the camera names ?name from the SPARQL endpoint at http://www.example1.org/sparql.

SELECT cType, name
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?cType ?name
     WHERE {
       ?s :cameraType ?cType
       SERVICE <http://www.example1.org/sparql>{ ?s :name ?name }
      }',
    SEM_Models('electronics'),
    null, null, null, null, ' '));

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.8.1 Privileges Required to Execute Federated SPARQL Queries

You need certain database privileges to use the SERVICE construct within SEM_MATCH queries. You should be granted EXECUTE privilege on the SPARQL_SERVICE MDSYS function by a user with DBA privileges: The following example grants this access to a user named RDFUSER:

grant execute on mdsys.sparql_service to rdfuser;

Also, an Access Control List (ACL) should be used to grant the CONNECT privilege to the user attempting a federated query. Example 1-69 creates a new ACL to grant the user RDFUSER the CONNECT privilege and assigns the domain * to the ACL. For more information about ACLs, see Oracle Database PL/SQL Packages and Types Reference.

Example 1-69 Access Control List and Host Assignment

dbms_network_acl_admin.create_acl (
  acl       => 'rdfuser.xml',
  description => 'Allow rdfuser to query SPARQL endpoints',
  principal => 'RDFUSER',
  is_grant  => true,
  privilege => 'connect'
);
 
dbms_network_acl_admin.assign_acl (
  acl  => 'rdfuser.xml',
  host => '*'
);

After the necessary privileges are granted, you are ready to execute federated queries from SEM_MATCH

Parent topic: Graph Patterns: Support for SPARQL 1.1 Federated Query

1.6.8.2 SPARQL SERVICE Join Push Down

The SPARQL SERVICE Join Push Down (SERVICE_JPDWN=T) feature can be used to improve the performance of certain SPARQL SERVICE queries. By default, the query pattern within the SERVICE clause is executed first on the remote SPARQL endpoint. The full result of this remote execution is then joined with the local portion of the query. This strategy can result in poor performance if the local portion of the query is very selective and the remote portion of the query is very unselective.

The SPARQL SERVICE Join Push Down feature cannot be used in a query that contains more than one SERVICE clause.

Example 1-70 SPARQL SERVICE Join Push Down

Example 1-70 shows the SPARQL SERVICE Join Push Down feature.

SELECT s, prop, obj
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?s ?prop ?obj
     WHERE {
       ?s rdf:type   :Camera .
       ?s :modelName "Camera 12345"
       SERVICE <http://www.example1.org/sparql> { ?s ?prop ?obj }
      }',
    SEM_Models('electronics'),
    null, null, null, null, ' SERVICE_JPDWN=T '));

In Example 1-70, the local portion of the query will return a very small number of rows, but the remote portion of the query is completely unbound and will return the entire remote dataset. When the SERVICE_JPDWN=T option is specified, SEM_MATCH performs a nested-loop style evaluation by first executing the local portion of the query and then executing a modified version of the remote query once for each row returned by the local portion. The remote query is modified with a FILTER clause that effectively performs a substitution for the join variable ?s. For example, if <urn:camera1> and <urn:camera2> are returned from the local portion of Example 1-70 as bindings for ?s, then the following two queries are sent to the remote endpoint: { ?s ?prop ?obj FILTER (?s = <urn:camera1>) } and { s ?prop ?obj FILTER (?s = <urn:camera2>) }.

Parent topic: Graph Patterns: Support for SPARQL 1.1 Federated Query

1.6.8.3 SPARQL SERVICE SILENT

When the SILENT keyword is used in federated queries, errors while accessing the specified remote SPARQL endpoint will be ignored. If the SERVICE SILENT request fails, a single solution with no bindings will be returned.

Example 1-71 uses SERVICE with the SILENT keyword inside an OPTIONAL clause, so that, when connection errors accessing http://www.example1.org/sparql appear, such errors will be ignored and all the rows retrieved from triple ?s :cameratype ?k will be combined with a null value for ?n.

Example 1-71 SPARQL SERVICE with SILENT Keyword

SELECT s, n
  FROM TABLE(SEM_MATCH(
    'PREFIX : <http://www.example.org/electronics/> 
     SELECT ?s ?n
     WHERE {
       ?s :cameraType ?k
       OPTIONAL { SERVICE SILENT <http://www.example1.org/sparql>{ ?k :name ?n } }
      }',
    SEM_Models('electronics'),
    null, null, null, null));

Parent topic: Graph Patterns: Support for SPARQL 1.1 Federated Query

1.6.8.4 Using a Proxy Server with SPARQL SERVICE

The following methods are available for sending SPARQL SERVICE requests through an HTTP proxy:

Specifying the HTTP proxy that should be used for requests in the current session. This can be done through the SET_PROXY function of UTL_HTTP package. Example 1-72 sets the proxy proxy.example.com to be used for HTTP requests, excluding those to hosts in the domain example2.com. (For more information about the SET_PROXY procedure, see Oracle Database PL/SQL Packages and Types Reference.)
Using the SERVICE_PROXY SEM_MATCH option, which allows setting the proxy address for SPARQL SERVICE request. However, in this case no exceptions can be specified, and all requests are sent to the given proxy server. Example 1-73 shows a SEM_MATCH query where the proxy address proxy.example.com at port 80 is specified.

Example 1-72 Setting Proxy Server with UTL_HTTP.SET_PROXY

BEGIN
  UTL_HTTP.SET_PROXY('proxy.example.com:80', 'example2.com');
END;
/

Example 1-73 Setting Proxy Server in SPARQL SERVICE

SELECT *
  FROM TABLE(SEM_MATCH(
    'SELECT *
     WHERE {
       SERVICE <http://www.example1.org/sparql>{ ?s ?p ?o }
      }',
    SEM_Models('electronics'),
    null, null, null, null, ' SERVICE_PROXY=proxy.example.com:80 '));

Parent topic: Graph Patterns: Support for SPARQL 1.1 Federated Query

1.6.8.5 Accessing SPARQL Endpoints with HTTP Basic Authentication

To allow accessing of SPARQL endpoints with HTTP Basic Authentication, user credentials should be saved in Session Context SDO_SEM_HTTP_CTX. A user with DBA privileges must grant EXECUTE on this context to the user that wishes to use basic authentication. The following example grants this access to a user named RDFUSER:

grant execute on mdsys.sdo_sem_http_ctx to rdfuser;

After the privilege is granted, the user should save the user name and password for each SPARQL Endpoint with HTTP Authentication through functions mdsys.sdo_sem_http_ctx.set_usr and mdsys.sdo_sem_http_ctx.set_pwd. The following example sets a user name and password for the SPARQL endpoint at http://www.example1.org/sparql:

BEGIN
  mdsys.sdo_sem_http_ctx.set_usr('http://www.example1.org/sparql','user');
  mdsys.sdo_sem_http_ctx.set_pwd('http://www.example1.org/sparql','pwrd');
END;
/

Parent topic: Graph Patterns: Support for SPARQL 1.1 Federated Query

1.6.9 Inline Query Optimizer Hints

In SEM_MATCH, the SPARQL comment construct has been overloaded to allow inline HINT0 query optimizer hints. In SPARQL, the hash (#) character indicates that the remainder of the line is a comment. To associate an inline hint with a particular BGP, place a HINT0 hint string inside a SPARQL comment and insert the comment between the opening curly bracket ({) and the first triple pattern in the BGP. Inline hints enable you to influence the execution plan for each BGP in a query.

Inline optimizer hints override any hints passed to SEM_MATCH through the options argument. For example, a global ALL_ORDERED hint applies to each BGP that does not specify an inline optimizer hint, but those BGPs with an inline hint use the inline hint instead of the ALL_ORDERED hint.

Example 1-74 Inline Query Optimizer Hints (BGP_JOIN)

The following example shows a query with inline query optimizer hints.

SELECT x, y, hp, cp
  FROM TABLE(SEM_MATCH(
    '{ # HINT0={ LEADING(t0) USE_NL(?x ?y ?bd) }
      ?x :grandParentOf ?y . ?x rdf:type :Male . ?x :birthDate ?bd
      OPTIONAL { # HINT0={ LEADING(t0 t1) BGP_JOIN(USE_HASH) }
                 ?x :homepage ?hp . ?x :cellPhoneNum ?cp }
     }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

The BGP_JOIN hint influences inter-BGP joins and has the following syntax: BGP_JOIN(<join_type>), where <join_type> is USE_HASH or USE_NL. Example 1-74 uses the BGP_JOIN(USE_HASH) hint to specify that a hash join should be used when joining the OPTIONAL BGP with its parent BGP.

Inline optimizer hints override any hints passed to SEM_MATCH through the options argument. For example, a global ALL_ORDERED hint applies to each BGP that does not specify an inline optimizer hint, but those BGPs with an inline hint use the inline hint instead of the ALL_ORDERED hint.

Example 1-75 Inline Query Optimizer Hints (ANTI_JOIN)

The ANTI_JOIN hint influences the evaluation of NOT EXISTS and MINUS clauses. This hint has the syntax ANTI_JOIN(<join_type>), where <join_type> is HASH_AJ, NL_AJ, or MERGE_AJ. The following example uses a hint to indicate that a hash anti join should be used. Global ALL_AJ_HASH, ALL_AJ_NL, ALL_AJ_MERGE can be used in the options argument of SEM_MATCH to influence the join type of all NOT EXISTS and MINUS clauses in the entire query.

SELECT x, y
  FROM TABLE(SEM_MATCH(
    'SELECT ?x ?y
     WHERE {
       ?x :grandParentOf ?y . ?x rdf:type :Male . ?x :birthDate ?bd
       FILTER ( 
         NOT EXISTS {# HINT0={ ANTI_JOIN(HASH_AJ) }
                     ?x :homepage ?hp . ?x :cellPhoneNum ?cp })
     }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

Example 1-76 Inline Query Optimizer Hints (NON_NULL)

HINT0={ NON_NULL} is supported in SPARQL SELECT clauses to signify that a particular variable is always bound (that is, has a non-null value in each result row). This hint allows the query compiler to optimize joins for values produced by SELECT expressions. These optimizations cannot be applied by default because it cannot be guaranteed that expressions will produce non-null values for all possible input. If you know that a SELECT expression will not produce any null values for a particular query, using this NON_NULL hint can significantly increase performance. This hint should be specified in the comment in a line before the 'AS' keyword of a SELECT expression.

The following example shows the NON_NULL hint option used in a SEM_MATCH query, specifying that variable ?full_name is definitely bound.

SELECT s, t
  FROM TABLE(SEM_MATCH(
    'SELECT * WHERE {
       ?s :name ?full_name
       { SELECT (CONCAT(?fname, " ", ?lname) # HINT0={ NON_NULL }
                 AS ?full_name)
         WHERE { 
           ?t :fname ?fname .
           ?t :lname ?lname } 
         } 
     }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
          null));

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.10 Full-Text Search

The Oracle-specific orardf:textContains SPARQL FILTER function uses full-text indexes on the MDSYS.RDF_VALUE$ table. This function has the following syntax (where orardf is a built-in prefix that expands to <http://xmlns.oracle.com/rdf/>):

orardf:textContains(variable, pattern)

The first argument to orardf:textContains must be a local variable (that is, a variable present in the BGP that contains the orardf:textContains filter), and the second argument must be a constant plain literal.

For example, orardf:textContains(x, y) returns true if x matches the expression y, where y is a valid expression for the Oracle Text SQL operator CONTAINS. For more information about such expressions, see Oracle Text Reference.

Before using orardf:textContains, you must create an Oracle Text index for the RDF network. To create such an index, invoke the SEM_APIS.ADD_DATATYPE_INDEX procedure as follows:

EXECUTE SEM_APIS.ADD_DATATYPE_INDEX('http://xmlns.oracle.com/rdf/text');

Performance for wildcard searches like orardf:textContains(?x, "%abc%") can be improved by using prefix and substring indexes. You can include any of the following options to the SEM_APIS.ADD_DATATYPE_INDEX procedure:

prefix_index=true – for adding prefix index
prefix_min_length=<number> – minimum length for prefix index tokens
prefix_max_length=<number> – maximum length for prefix index tokens
substring_index=true – for adding substring index

For more information about Oracle Text indexing elements, see Oracle Text Reference.

When performing large bulk loads into a semantic network with a text index, the overall load time may be faster if you drop the text index, perform the bulk load, and then re-create the text index. See Using Data Type Indexes for more information about data type indexing.

After creating a text index, you can use the orardf:textContains FILTER function in SEM_MATCH queries. Example 1-77 uses orardf:textContains to find all grandfathers whose names start with the letter A or B.

Example 1-77 Full-Text Search

SELECT x, y, n
  FROM TABLE(SEM_MATCH(
    '{ ?x :grandParentOf ?y . ?x rdf:type :Male . ?x :name ?n 
       FILTER (orardf:textContains(?n, " A% | B% ")) }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
          null));

Example 1-78 orardf:textScore

The ancillary operator orardf:textScore can be used in combination with orardf:textContains to rank results by the goodness of their text match. There are, however, limitations when using orardf:textScore. The orardf:textScore invocation must appear as a SELECT expression in the SELECT clause immediately surrounding the basic graph pattern that contains the corresponding orardf:textContains FILTER. The alias for this SELECT expression can then be used in other parts of the query. In addition, a REWRITE=F' query hint must be used in the options argument of SEM_MATCH.

The following example finds text matches with score greater than 0.5. Notice that an additional invocation id argument is required for orardf:textContains, so that it can be linked to the orardf:textScore invocation with the same invocation id. The invocation ID is an arbitrary integer constant used to match a primary operator with its ancillary operator.

SELECT x, y, n, scr
  FROM TABLE(SEM_MATCH(
    'SELECT *
     WHERE {
       { SELECT ?x ?y ?n (orardf:textScore(123) AS ?scr)
         WHERE { 
           ?x :grandParentOf ?y . ?x rdf:type :Male . ?x :name ?n 
           FILTER (orardf:textContains(?n, " A% | B% ", 123)) }
       }
       FILTER (?scr > 0.5)
     }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),           
    null,
    null,
    ' REWRITE=F '));

Example 1-79 orardf:like

For a lightweight text search, you can use the orardf:like function, which performs simple test for pattern matching using the Oracle SQL operator LIKE. The orardf:like function has the following syntax:

orardf:like(string, pattern

The first argument of orardf:like can be any variable or RDF term, as opposed to orardf:Contains, which requires the first argument to be a local variable. When the first argument to orardf:like is a URI, the match is performed against the URI suffix only. The second argument must be a pattern expression, which can contain the following special pattern-matching characters:

The percent sign (%) can match zero or more characters.
The underscore (_) matches exactly one character.

The following example shows a percent sign (%) wildcard search to find all grandparents whose URIs start with Ja.

SELECT x, y, n
  FROM TABLE(SEM_MATCH(
    '{ ?x :grandParentOf ?y . ?y :name ?n 
        FILTER (orardf:like(?x, "Ja%")) }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

The following example shows an underscore (_) wildcard search to find all the grandchildren whose names start with J followed by two characters and end with k..

SELECT x, y, n
  FROM TABLE(SEM_MATCH(
    '{ ?x :grandParentOf ?y . ?y :name ?n 
        FILTER (orardf:like(?n, "J__k")) 
    }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

For efficient execution of orardf:like, you can create an index using the SEM_APIS.ADD_DATATYPE_INDEX procedure with http://xmlns.oracle.com/rdf/like as the data type URI. This index can speed up queries when the first argument is a local variable and the leading character of the search pattern is not a wildcard. The underlying index is a simple function-based B-Tree index on a varchar function, which has lower maintenance and storage costs than a full Oracle Text index. The index for orardf:like is created as follows:

EXECUTE SEM_APIS.ADD_DATATYPE_INDEX('http://xmlns.oracle.com/rdf/like');

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.11 Spatial Support

RDF Semantic Graph supports storage and querying of spatial geometry data through the OGC GeoSPARQL standard and through Oracle-specific SPARQL extensions. Geometry data can be stored as orageo:WKTLiteral, ogc:wktLiteral, or ogc:gmlLiteral typed literals, and geometry data can be queried using several query functions for spatial operations. Spatial indexing for increased performance is also supported.

orageo is a built-in prefix that expands to <http://xmlns.oracle.com/rdf/geo/>, ogc is a built-in prefix that expands to <http://www.opengis.net/ont/geosparql#>, and ogcf is a built-in prefix that expands to <http://www.opengis.net/def/function/geosparql>.

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.11.1 OGC GeoSPARQL Support

RDF Semantic Graph supports the following conformance classes for the OGC GeoSPARQL standard (http://www.opengeospatial.org/standards/geosparql) using well-known text (WKT) serialization and the Simple Features relation family.

Core
Topology Vocabulary Extension (Simple Features)
Geometry Extension (WKT, 1.2.0)
Geometry Topology Extension (Simple Features, WKT, 1.2.0)
RDFS Entailment Extension (Simple Features, WKT, 1.2.0)

Specifics for representing and querying spatial data using GeoSPARQL are covered in sections that follow this one.

Parent topic: Spatial Support

1.6.11.2 Representing Spatial Data in RDF

Spatial geometries can be represented in RDF as orageo:WKTLiteral, ogc:wktLiteral, or ogc:gmlLiteral typed literals.

Example 1-80 Spatial Point Geometry Represented as orageo:WKTLiteral

The following example shows the orageo:WKTLiteral encoding for a simple point geometry.

"Point(-83.4 34.3)"^^<http://xmlns.oracle.com/rdf/geo/WKTLiteral>

Example 1-81 Spatial Point Geometry Represented as ogc:wktLiteral

The following example shows the ogc:wktLiteral encoding for the same point as in the preceding example.

"Point(-83.4 34.3)"^^<http://www.opengis.net/ont/geosparql#wktLiteral>

Both orageo:WKTLiteral and ogc:wktLiteral encodings consist of an optional spatial reference system URI, followed by a Well-Known Text (WKT) string that encodes a geometry value. The spatial reference system URI and the WKT string should be separated by a whitespace character. (In this document the term geometry literal is used to refer to both orageo:WKTLiteral and ogc:wktLiteral typed literals.)

Supported spatial reference system URIs have the following form <http://www.opengis.net/def/crs/EPSG/0/{srid}>, where {srid} is a valid spatial reference system ID defined by the European Petroleum Survey Group (EPSG). For URIs that are not in the EPSG Geodetic Parameter Dataset, the spatial reference system URIs used have the form <http://xmlns.oracle.com/rdf/geo/srid/{srid}>., where {srid} is a valid spatial reference system ID from Oracle Spatial and Graph. If a geometry literal value does not include a spatial reference system URI, then the default spatial reference system, WGS84 Longitude-Latitude (URI <http://www.opengis.net/def/crs/OGC/1.3/CRS84>), is used. The same default spatial reference system is used when geometry literal values are encountered in a query string.

Example 1-82 Spatial Point Geometry Represented as ogc:gmlLiteral

The following example shows the ogc:gmlLiteral encoding for a point geometry.

"<gml:Point srsName=\"urn:ogc:def:crs:EPSG::8307\" xmlns:gml=\"http://www.opengis.net/gml\"><gml:posList srsDimension=\"2\">-83.4 34.3</gml:posList></gml:Point>"^^<http://www.opengis.net/ont/geosparql#gmlLiteral>

ogc:gmlLiteral encodings consist of a valid element from the GML schema that implements a subtype of GM_Object. In contrast to WKT literals, A GML encoding explicitly includes spatial reference system information, so a spatial reference system URI prefix is not needed.

Several geometry types can be represented as geometry literal values, including point, linestring, polygon, polyhedral surface, triangle, TIN, multipoint, multi-linestring, multipolygon, and geometry collection. Up to 500,000 vertices per geometry are supported for two-dimensional geometries.

Example 1-83 Spatial Data Encoded Using orageo:WKTLiteral Values

The following example shows some RDF spatial data (in N-triple format) encoded using orageo:WKTLiteral values. In this example, the first two geometries (in lot1) use the default coordinate system (SRID 8307), but the other two geometries (in lot2) specify SRID 8265.

# spatial data for lot1 using the default WGS84 Longitude-Latitude spatial reference system
<urn:lot1> <urn:hasExactGeometry> "Polygon((-83.6 34.1, -83.6 34.5, -83.2 34.5, -83.2 34.1, -83.6 34.1))"^^<http://xmlns.oracle.com/rdf/geo/WKTLiteral> .
<urn:lot1> <urn:hasPointGeometry> "Point(-83.4 34.3)"^^<http://xmlns.oracle.com/rdf/geo/WKTLiteral> .
# spatial data for lot2 using the NAD83 Longitude-Latitude spatial reference system
<urn:lot2> <urn:hasExactGeometry> "<http://xmlns.oracle.com/rdf/geo/srid/8265> Polygon((-83.6  34.1, -83.6 34.3, -83.4 34.3, -83.4 34.1, -83.6 34.1))"^^<http://xmlns.oracle.com/rdf/geo/WKTLiteral> .
<urn:lot2> <urn:hasPointGeometry> "<http://xmlns.oracle.com/rdf/geo/srid/8265> Point(-83.5 34.2)"^^<http://xmlns.oracle.com/rdf/geo/WKTLiteral> .

For more information, see the chapter about coordinate systems (spatial reference systems) in Oracle Spatial and Graph Developer's Guide. See also the material about the WKT geometry representation in the Open Geospatial Consortium (OGC) Simple Features document, available at: http://www.opengeospatial.org/standards/sfa

Parent topic: Spatial Support

1.6.11.3 Validating Geometries

Before manipulating spatial data, you should check that there are no invalid geometry literals stored in your RDF model. The procedure SEM_APIS.VALIDATE_GEOMETRIES allows verifying geometries in an RDF model. The geometries are validated using an input SRID and tolerance value. (SRID and tolerance are explained in Indexing Spatial Data.)

If there are invalid geometries, a table with name {model_name}_IVG$, is created in the user schema, where {model_name} is the name of the RDF model specified. Such table contains, for each invalid geometry literal, the value_id of the geometry literal in MDSY.RDF_VALUE$ table, the error message explaining the reason the geometry is not valid and a corrected geometry literal if the geometry can be rectified. For more information about geometry validation, see the reference information for the Oracle Spatial and Graph subprograms SDO_GEOM.VALIDATE_GEOMETRY_WITH_CONTEXT and SDO_GEOM.VALIDATE_LAYER_WITH_CONTEXT.

Example 1-84 Validating Geometries in a Model

The following example validates a model m, using SRID=8307 and tolerance=0.1.

-- Validate
EXECUTE sem_apis.validate_geometries(model_name=>'m',SRID=>8307,tolerance=>0.1);
-- Check for invalid geometries
SELECT original_vid, error_msg, corrected_wkt_literal FROM M_IVG$;

Parent topic: Spatial Support

1.6.11.4 Indexing Spatial Data

Before you can use any of the SPARQL extension functions (introduced in Querying Spatial Data) to query spatial data, you must create a spatial index on the RDF network by calling the SEM_APIS.ADD_DATATYPE_INDEX procedure.

When you create the spatial index, you must specify the following information:

SRID - The ID for the spatial reference system in which to create the spatial index. Any valid spatial reference system ID from Oracle Spatial and Graph can be used as an SRID value.
TOLERANCE – The tolerance value for the spatial index. Tolerance is a positive number indicating how close together two points must be to be considered the same point. The units for this value are determined by the default units for the SRID used (for example, meters for WGS84 Long-Lat). Tolerance is explained in detail in Oracle Spatial and Graph Developer's Guide.
DIMENSIONS - A text string encoding dimension information for the spatial index. Each dimension is represented by a sequence of three comma-separated values: name, minimum value, and maximum value. Each dimension is enclosed in parentheses, and the set of dimensions is enclosed by an outer parenthesis.

Example 1-85 Adding a Spatial Data Type Index on RDF Data

Example 1-85 adds a spatial data type index on the RDF network, specifying the WGS84 Longitude-Latitude spatial reference system, a tolerance value of 10 meters, and the recommended dimensions for the indexing of spatial data that uses this coordinate system. The TOLERANCE, SRID, and DIMENSIONS keywords are case sensitive, and creating a data type index for <http://xmlns.oracle.com/rdf/geo/WKTLiteral> will also index <http://www.opengis.net/ont/geosparql#wktLiteral> geometry literals, and vice versa (that is, creating a data type index for <http://www.opengis.net/ont/geosparql#wktLiteral> will also index <http://xmlns.oracle.com/rdf/geo/WKTLiteral> geometry literals).

EXECUTE sem_apis.add_datatype_index('http://xmlns.oracle.com/rdf/geo/WKTLiteral',  options=>'TOLERANCE=10 SRID=8307 DIMENSIONS=((LONGITUDE,-180,180) (LATITUDE,-90,90))');

No more than one spatial data type index is supported for an RDF network. Geometry literal values stored in the RDF network are automatically normalized to the spatial reference system used for the index, so a single spatial index can simultaneously support geometry literal values from different spatial reference systems. This coordinate transformation is done transparently for indexing and spatial computations. When geometry literal values are returned from a SEM_MATCH query, the original, untransformed geometry is returned.

For more information about spatial indexing, see the chapter about indexing and querying spatial data in Oracle Spatial and Graph Developer's Guide.

Parent topic: Spatial Support

1.6.11.5 Querying Spatial Data

Several SPARQL extension functions are available for performing spatial queries in SEM_MATCH. For example, for spatial RDF data, you can find the area and perimeter (length) of a geometry, the distance between two geometries, and the centroid and the minimum bounding rectangle (MBR) of a geometry, and you can check various topological relationships between geometries.

SEM_MATCH Support for Spatial Queries contains reference and usage information about the available functions, grouped into two categories:

GeoSPARQL functions
Oracle-specific functions

Parent topic: Spatial Support

1.6.11.6 Using Long Literals with GeoSPARQL Queries

Geometry literals can become very long, which make the use of CLOBs necessary to represent them. CLOB constants cannot be used directly in a SEM_MATCH query. However, a user-defined SPARQL function can be used to bind CLOB constants into SEM_MATCH queries.

The following example does this by using a temporary table.

Example 1-86 Binding a CLOB Constant into a SPARQL Query

conn rdfuser/<password>;

-- Create temporary table
create global temporary table local_value$(
 VALUE_TYPE 	 VARCHAR2(10),
 VALUE_NAME     VARCHAR2(4000),
 LITERAL_TYPE	 VARCHAR2(1000),
 LANGUAGE_TYPE	 VARCHAR2(80),
 LONG_VALUE 	 CLOB)
on commit preserve rows;

-- Create user-defined function to transform a CLOB into an RDF term
CREATE OR REPLACE FUNCTION myGetClobTerm
RETURN MDSYS.SDO_RDF_TERM
AS
  term	     SDO_RDF_TERM;
BEGIN
  select sdo_rdf_term(
      value_type,
      value_name,
      literal_type,
      language_type,
      long_value)
  into term
  from local_value$
  where rownum < 2;

  RETURN term;
END;
/

-- Insert a row with CLOB geometry
insert into local_value$(value_type,value_name,literal_type,language_type,long_value)
values ('LIT','','http://www.opengis.net/ont/geosparql#wktLiteral','','Some_CLOB_WKT');

-- Use the CLOB constant in a SEM_MATCH query
SELECT cdist
FROM table(sem_match(
'{ ?cdist ogc:asWKT ?cgeom 
   FILTER (
     orageo:withinDistance(?cgeom, oraextf:myGetClobTerm(), 200, "M")) }'
,sem_models('gov_all_vm')
,null, null,null, null, ' ALLOW_DUP=T '));

Parent topic: Spatial Support

1.6.12 Flashback Query Support

You can perform SEM_MATCH queries that return past data using Flashback Query. A TIMESTAMP or a System Change Number (SCN) value is passed to SEM_MATCH through the AS_OF hint. The AS_OF hint can have one of the following forms:

AS_OF[TIMESTAMP,<TIMESTAMP_VALUE>], where <TIMESTAMP_VALUE> is a valid timestamp string with format 'YYYY/MM/DD HH24:MI:SS.FF'.
AS_OF[SCN,<SCN_VALUE>], where <SCN_VALUE> is a valid SCN.

The AS_OF hint is internally transformed to perform a Flashback Query (SELECT AS OF) against the queried table or view containing triples of the specified model. This allows you to query the model as it existed in a prior time. For this feature to work, the invoker needs a flashback privilege on the queried metadata table or view (MDSYS.RDFM_model-name view for native models, MDSYS.SEMU_virtual-model-name and MDSYS.SEMV_virtual-model-name for virtual models, and underlying relational tables for RDF view models). For example: grant flashback on MDSYS.RDFM_FAMILY to rdfuser

Restrictions on Using Flashback Query with RDF Data

Adding or removing a partition from a partitioned table disables Flashback Query for previous versions of the partitioned table. As a consequence, creating or dropping a native RDF model or creating or dropping an entailment will disable Flashback Query for previous versions of all native RDF models in a semantic network. Therefore, be sure to control such operations when using Flashback Query in a semantic network.

Example 1-87 Flashback Query Using TIMESTAMP

The following example shows the use of the AS_OF clause defining a TIMESTAMP.

SELECT x, name
  FROM TABLE(SEM_MATCH(
    '{ ?x :name ?name }',
    SEM_Models('family'),
    null, 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null,null,' AS_OF=[TIMESTAMP,2016/05/02 13:06:03.979546]'));

Example 1-88 Flashback Query Using SCN

The following example shows the use of the AS_OF clause specifying an SCN.

SELECT x, name
  FROM TABLE(SEM_MATCH(
    '{ ?x :name ?name }',
    SEM_Models('family'),
    null, 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null,null,' AS_OF=[SCN,1429849]'));

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.13 Best Practices for Query Performance

This section describes some recommended practices for using the SEM_MATCH table function to query semantic data. It includes the following subsections:

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.6.13.1 FILTER Constructs Involving xsd:dateTime, xsd:date, and xsd:time

By default, SEM_MATCH complies with the XML Schema standard for comparison of xsd:date, xsd:time, and xsd:dateTime values. According to this standard, when comparing two calendar values c1 and c2 where c1 has an explicitly specified time zone and c2 does not have a specified time zone, c2 is converted into the interval [c2-14:00, c2+14:00]. If c2-14:00 <= c1 <= c2+14:00, then the comparison is undefined and will always evaluate to false. If c1 is outside this interval, then the comparison is defined.

However, the extra logic required to evaluate such comparisons (value with a time zone and value without a time zone) can significantly slow down queries with FILTER constructs that involve calendar values. For improved query performance, you can disable this extra logic by specifying FAST_DATE_FILTER=T in the options parameter of the SEM_MATCH table function. When FAST_DATE_FILTER=T is specified, all calendar values without time zones are assumed to be in Greenwich Mean Time (GMT).

Note that using FAST_DATE_FILTER=T does not affect query correctness when either (1) all calendar values in the data set have a time zone or (2) all calendar values in the data set do not have a time zone.

Parent topic: Best Practices for Query Performance

1.6.13.2 Function-Based Indexes for FILTER Constructs Involving Typed Literals

The evaluation of SEM_MATCH queries involving the FILTER construct often requires executing one or more SQL functions against the RDF_VALUE$ table. For example, the filter (?x < "1929-11-16Z"^^xsd:date) invokes the SEM_APIS.GETV$DATETZVAL function.

Function-based indexes can be used to improve the performance of queries that contain a filter condition involving a typed literal. For example, an xsd:date function-based index may speed up evaluation of the filter (?x < "1929-11-16Z"^^xsd:date).

Convenient interfaces are provided for creating, altering, and dropping these function-based indexes. For more information, see Using Data Type Indexes.

Note, however, that the existence of these function-based indexes on the MDSYS.RDF_VALUE$ table can significantly slow down bulk load operations. In many cases it may be faster to drop the indexes, perform the bulk load, and then re-create the indexes, as opposed to doing the bulk load with the indexes in place.

Parent topic: Best Practices for Query Performance

1.6.13.3 FILTER Constructs Involving Relational Expressions

The following recommendations apply to FILTER constructs involving relational expressions:

The sameCanonTerm extension function is the most efficient way to compare two RDF terms for equality because it allows an id-based comparison in all cases.
When using standard SPARQL features, the sameTerm built-in function is more efficient than using = or != when comparing two variables in a FILTER clause, so (for example) use sameTerm(?a, ?b) instead of (?a = ?b) and use (!sameTerm(?a, ?b)) instead of (?a != ?b) whenever possible.
When comparing values in FILTER expressions, you may get better performance by reducing the use of negation. For example, it is more efficient to evaluate (?x <= "10"^^xsd:int) than it is to evaluate the expression (!(?x > "10"^^xsd:int)).

Parent topic: Best Practices for Query Performance

1.6.13.4 Optimizer Statistics and Dynamic Sampling

Having sufficient statistics for the query optimizer is critical for good query performance. In general, you should ensure that you have gathered basic statistics for the semantic network using the SEM_PERF.GATHER_STATS procedure (described in SEM_PERF Package Subprograms).

Due to the inherent flexibility of the RDF data model, static information may not produce optimal execution plans for SEM_MATCH queries. Dynamic sampling can often produce much better query execution plans. Dynamic sampling levels can be set at the session or system level using the optimizer_dynamic_sampling parameter, and at the individual query level using the dynamic_sampling(level) SQL query hint. In general, it is good to experiment with dynamic sampling levels between 3 and 6. For information about estimating statistics with dynamic sampling, see Oracle Database SQL Tuning Guide.

Example 1-89 uses a SQL hint for a dynamic sampling level of 6.

Example 1-89 SQL Hint for Dynamic Sampling

SELECT /*+ DYNAMIC_SAMPLING(6) */ x, y
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . 
      ?x rdf:type :Male . 
      ?x :birthDate ?bd }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null, null, '' ));

Parent topic: Best Practices for Query Performance

1.6.13.5 Multi-Partition Queries

The following recommendations apply to the use of multiple semantic models, semantic models plus entailments, and virtual models:

If you execute SEM_MATCH queries against multiple semantic models or against semantic models plus entailments, you can probably improve query performance if you create a virtual model (see Virtual Models) that contains all the models and entailments you are querying and then query this single virtual model.
Use the ALLOW_DUP=T query option. If you do not use this option, then an expensive (in terms of processing) duplicate-elimination step is required during query processing, in order to maintain set semantics for RDF data. However, if you use this option, the duplicate-elimination step is not performed, and this results in significant performance gains.

Parent topic: Best Practices for Query Performance

1.6.13.6 Compression on Systems with OLTP Index Compression

On systems where OLTP index compression is supported (such as Exadata). you can take advantage of the feature to improve the compression ratio for some of the B-tree indexes used by the semantic network.

For example, a DBA can use the following command to change the compression scheme on the MDSYS.RDF_VAL_NAMETYLITLNG_IDX index from prefix compression to OLTP index compression:

SQL> alter index mdsys.RDF_VAL_NAMETYLITLNG_IDX rebuild compress for oltp high;

Parent topic: Best Practices for Query Performance

1.6.13.7 Unbounded Property Path Expressions

A depth-limited search should be used for + and * property path operators whenever possible. The depth-limited implementation for * and + is likely to significantly outperform the CONNECT BY-based implementation in large and/or highly connected graphs. A depth limit of 10 is used by default. For a given graph, depth limits larger than the graph's diameter are not useful. See Property Paths for more information on setting depth limits.

A backward chaining style inference using rdfs:subClassOf+ for ontologies with very deep class hierarchies may be an exception to this rule. In such cases, unbounded CONNECT BY-based evaluations may perform better than depth-limited evaluations with very high depth limits (for example, 50).

Parent topic: Best Practices for Query Performance

1.6.13.8 Nested Loop Pushdown for Property Paths

If an unbounded CONNECT BY evaluation is performed for a property path, and if the subject of the property path triple pattern is a variable, a CONNECT BY WITHOUT FILTERING operation will most likely be used. If this subject variable is only bound to a small number of values during query execution, a nested loop strategy (see Nested Loop Pushdown with Overloaded Service) could be a good option to run the query. In this case, the property path can be pushed down into an overloaded SERVICE clause and the OVERLOADED_NL=T hint can be used.

For example, consider the following query where there is an unbounded property path search { ?s :hasManager+ ?x }, but the triple { ?s :ename "ADAMS" } only has a small number of possible values for ?s.

select s, x
from table(sem_match(
'PREFIX : <http://scott-hr.org#>
 SELECT *
 WHERE {
   ?s :ename "ADAMS" .
   ?s :hasManager+ ?x .
 }',
sem_models('scott_hr_data'),
null,null,null,null,'  ALL_MAX_PP_DEPTH(0) '));

The query can be transformed to force the nested-loop strategy. Notice that the model specified in the SERVICE graph is the same as the model specified in the SEM_MATCH call.

select s, x
from table(sem_match(
'PREFIX : <http://scott-hr.org#>
 SELECT *
 WHERE {
   ?s :ename "ADAMS" .
   service oram:scott_hr_data { ?s :hasManager+ ?x . }
 }',
sem_models('scott_hr_data'),
null,null,null,null,'  ALL_MAX_PP_DEPTH(0) OVERLOADED_NL=T '));

With this nested-loop strategy, { ?s :hasManager_ ?x } is evaluated once for each value of ?s, and in each evaluation, a constant value is substituted for ?s. This constant in the subject position allows a CONNECT BY WITH FILTERING operation, which usually provides a substantial performance improvement.

Parent topic: Best Practices for Query Performance

1.6.13.9 Grouping and Aggregation

MIN, MAX and GROUP_CONCAT aggregates require special logic to fully capture SPARQL semantics for input of non-uniform type (for example, MAX(?x)). For certain cases where a uniform input type can be determined at compile time (for example, MAX(STR(?x)) – plain literal input), optimizations for built-in SQL aggregates can be used. Such optimizations generally give an order of magnitude increase in performance. The following cases are optimized:

MIN/MAX(<plain literal>)
MIN/MAX(<numeric>)
MIN/MAX(<dateTime>)
GROUP_CONCAT(<plain literal>)

Example 1-90 uses MIN/MAX(<numeric>) optimizations.

Example 1-90 Aggregate Optimizations

SELECT dept, minSal, maxSal
  FROM TABLE(SEM_MATCH(
    'SELECT ?dept (MIN(xsd:decimal(?sal)) AS ?minSal) (MAX(xsd:decimal(?sal)) AS ?maxSal)
     WHERE
       {?x :salary ?y . 
        ?x :department ?dept }
     GROUP BY ?dept',
    SEM_Models('hr_data'),
    null, null, null, null, '' ));

Parent topic: Best Practices for Query Performance

1.6.13.10 Use of Bind Variables to Reduce Compilation Time

For some queries, query compilation can be more expensive than query execution, which can limit throughput on workloads of small queries. If the queries in your workload differ only in the constants used, then session context-based bind variables can be used to skip the compilation step.

The following example shows how to use a session context in combination with a user-defined SPARQL function to compile a SEM_MATCH query once and then run it with different constants. The basic idea is to create a user-defined function that reads an RDF term value from the session context and returns it. A SEM_MATCH query with this function will read the RDF term value at run time; so when the session context variable changes, the same exact SEM_MATCH query will see a different value.

conn / as sysdba;
grant create any context to testuser;

conn testuser/testuser;

create or replace package MY_CTXT_PKG as  
  procedure set_attribute(name varchar2, value varchar2);  
  function get_attribute(name varchar2) return varchar2;  
end MY_CTXT_PKG;  
/

create or replace package body MY_CTXT_PKG as  
  procedure set_attribute(  
    name varchar2,  
    value varchar2  
  ) as  
  begin  
    dbms_session.set_context(namespace => 'MY_CTXT',  
                             attribute => name,  
                             value     => value );
  end;  

  function get_attribute(  
    name varchar2  
  ) return varchar2 as  
  begin  
    return sys_context('MY_CTXT', name);  
  end;  
end MY_CTXT_PKG;  
/

create or replace function myCtxFunc(  
  params in MDSYS.SDO_RDF_TERM_LIST  
) return MDSYS.SDO_RDF_TERM  
as  
  name varchar2(4000);  
  arg  MDSYS.SDO_RDF_TERM;  
begin  
  arg := params(1);  
  name := arg.value_name;  
  return MDSYS.SDO_RDF_TERM(my_ctxt_pkg.get_attribute(name));  
end;  
/  

CREATE OR REPLACE CONTEXT MY_CTXT using TESTUSER.MY_CTXT_PKG;

-- Set a value
exec MY_CTXT_PKG.set_attribute('value','<http://www.example.org/family/Martha>');

-- Query using the function
-- Note the use of HINT0={ NON_NULL } to allow the most efficient join
SELECT s, p, o
  FROM TABLE(SEM_MATCH(
    'SELECT ?s ?p ?o
     WHERE {
       BIND (oraextf:myCtxFunc("value") # HINT0={ NON_NULL } 
             AS ?s)
       ?s ?p ?o }',
    SEM_Models('family'),
    null,
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

-- Set another value
exec MY_CTXT_PKG.set_attribute('value','<http://www.example.org/family/Sammy>');

-- Now the same query runs for Sammy without recompiling
SELECT s, p, o
  FROM TABLE(SEM_MATCH(
    'SELECT ?s ?p ?o
     WHERE {
       BIND (oraextf:myCtxFunc("value") # HINT0={ NON_NULL } 
             AS ?s)
       ?s ?p ?o }',
    SEM_Models('family'),
    null,
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));

Parent topic: Best Practices for Query Performance

1.6.13.11 Non-Null Expression Hints

When performing a join of several graph patterns with common variables that can be unbound, a more complex join condition is needed to handle null values to avoid performance degradation. Unbound values can be introduced through SELECT expressions, binds, OPTIONAL clauses, and unions. In many cases, SELECT expressions are not expected to produce NULL values. In such cases, query performance can be substantially improved through use of an inline HINT0={ NON_NULL } hint to mark a specific SELECT expression as definitely non-null or through use of a DISABLE_NULL_EXPR_JOIN query option to signify that all SELECT expressions produce only non-null values.

The following example includes the global DISABLE_NULL_EXPR_JOIN hint to signify that variable ?fulltitle is always bound on both sides of the join. (See also Inline Query Optimizer Hints.)

SELECT s, t
  FROM TABLE(SEM_MATCH(
    'SELECT * WHERE {
       { SELECT ?s (CONCAT(?title, ". ", ?fullname) AS ?fulltitle)
         WHERE { ?s :fullname ?fullname .
                 ?s :title ?title }
       }
       { SELECT ?t (CONCAT(?title, ". ", ?fname, " ", ?lname) AS ?fulltitle)
         WHERE { 
         ?t :fname ?fname .
         ?t :lname ?lname . 
         ?t :title ?title } 
       } 
     }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null,
    null,
    ' DISABLE_NULL_EXPR_JOIN '));

Parent topic: Best Practices for Query Performance

1.6.14 Special Considerations When Using SEM_MATCH

The following considerations apply to SPARQL queries executed by RDF Semantic Graph using SEM_MATCH:

Value assignment
- A compile-time error is raised when undefined variables are referenced in the source of a value assignment.
Grouping and aggregation
- Non-grouping variables (query variables not used for grouping and therefore not valid for projection) cannot be reused as a target for value assignment.
- Non-numeric values are ignored by the AVG and SUM aggregates.
- By default, SEM_MATCH returns no rows for an aggregate query with a graph pattern that fails to match. The W3C specification requires a single, null row for this case. W3C-compliant behavior can be obtained with the STRICT_AGG_CARD=T query option for a small performance penalty.
ORDER BY
- When using SPARQL ORDER BY in SEM_MATCH, the containing SQL query should be ordered by SEM$ROWNUM to ensure that the desired ordering is maintained through any enclosing SQL blocks.
Numeric computations
- The native Oracle NUMBER type is used internally for all arithmetic operations, and the results of all arithmetic operations are serialized as xsd:decimal. Note that the native Oracle NUMBER type is more precise than both BINARY_FLOAT and BINARY_DOUBLE. See Oracle Database SQL Language Reference for more information on the NUMBER built-in data type.
- Division by zero causes a runtime error instead of producing an unbound value.
Negation
- EXISTS and NOT EXISTS filters that reference potentially unbound variables are not supported in the following contexts:
  - Non-aliased expressions in GROUP BY
  - Input to aggregates
  - Expressions in ORDER BY
  - FILTER expressions within OPTIONAL graph patterns that also reference variables that do not appear inside of the OPTIONAL graph pattern
  The first three cases can be realized by first assigning the result of the EXISTS or NOT EXISTS filter to a variable using a BIND clause or SELECT expression.
  
  These restrictions do not apply to EXISTS and NOT EXISTS filters that only reference definitely bound variables.
Blank nodes
- Blank nodes are not supported within graph patterns.
- The BNODE(literal) function returns the same blank node value every time it is called with the same literal argument.
Property paths
- Unbounded operators + and * use a 10-hop depth limit by default for performance reasons. This behavior can be changed to a truly unbounded search by setting a depth limit of 0. See Property Paths for details.
Long literals (CLOBs)
- SPARQL functions and aggregates do not support long literals by default.
- Specifying the CLOB_EXP_SUPPORT=T query option enables long literal support for the following SPARQL functions: IF, COALESCE, STRLANG, STRDT, SUBSTR, STRBEFORE, STRAFTER, CONTAINS, STRLEN, STRSTARTS, STRENDS.
- Specifying the CLOB_AGG_SUPPORT=T query option enables long literal support for the following aggregates: MIN, MAX, SAMPLE, GROUP_CONCAT.
Canonicalization of RDF literals
- By default, RDF literals returned from SPARQL functions and constant RDF literals used in value assignment statements (BIND, SELECT expressions, GROUP BY expressions) are canonicalized. This behavior is consistent with the SPARQL 1.1 D-Entailment Regime.
- Canonicalization can be disabled with the PROJ_EXACT_VALUES=T query option.

Parent topic: Using the SEM_MATCH Table Function to Query Semantic Data

1.7 Using the SEM_APIS.SPARQL_TO_SQL Function to Query Semantic Data

You can use the SEM_APIS.SPARQL_TO_SQL function as an alternative to the SEM_MATCH table function to query semantic data.

The SEM_APIS.SPARQL_TO_SQL function is provided as an alternative to the SEM_MATCH table function. It can be used by application developers to obtain the SQL translation for a SPARQL query. This is the same SQL translation that would be executed by SEM_MATCH. The resulting SQL translation can then be executed in the same way as any other SQL string (for example, with EXECUTE IMMEDIATE in PL/SQL applications or with JDBC in Java applications).

The first (sparql_query) parameter to SEM_APIS.SPARQL_TO_SQL specifies a SPARQL query string and corresponds to the query argument of SEM_MATCH. In this case, however, sparql_query is of type CLOB, which allows query strings longer than 4000 bytes (or 32K bytes with long VARCHAR enabled). All other parameters are exactly equivalent to the same arguments of SEM_MATCH (described in Using the SEM_MATCH Table Function to Query Semantic Data). The SQL query string returned by SEM_APIS.SPARQL_TO_SQL will produce the same return columns as an execution of SEM_MATCH with the same arguments.

The following PL/SQL fragment is an example of using the SEM_APIS.SPARQL_TO_SQL function.

DECLARE
  c           sys_refcursor;
  sparql_stmt clob;
  sql_stmt    clob;
  x_value     varchar2(4000);
BEGIN
  sparql_stmt := 
    'SELECT ?x
     WHERE {
       ?x :grandParentOf ?y . 
       ?x rdf:type :Male
     }';

  sql_stmt := sem_apis.sparql_to_sql(
                sparql_stmt,
                sem_models('family'),
                SEM_Rulebases('RDFS','family_rb'),
                SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
                null,
                ' PLUS_RDFT=VC ');

  open c for 'select x$rdfterm from(' || sql_stmt || ')';
  loop
    fetch c into x_value;
    exit when c%NOTFOUND;
    
    dbms_output.put_line('x_value: ' || x_value);    
  end loop;
  close c;

END;
/

Parent topic: RDF Semantic Graph Overview

1.7.1 Using Bind Variables with SEM_APIS.SPARQL_TO_SQL

The SEM_APIS.SPARQL_TO_SQL function allows the use of PL/SQL and JDBC bind variables. This is possible because the SQL translation returned from SEM_APIS.SPARQL_TO_SQL does not involve an ANYTYPE table function invocation. The basic strategy is to transform simple SPARQL BIND clauses into either JDBC or PL/SQL bind variables when the USE_BIND_VAR=PLSQL or USE_BIND_VAR=JDBC query option is specified. A simple SPARQL BIND clause is one with the form BIND (<constant> AS ?var).

With the bind variable option, the SQL translation will contain two bind variables for each transformed SPARQL query variable: one for the value ID, and one for the RDF term string. An RDF term value can be substituted for a SPARQL query variable by specifying the value ID (from MDSYS.RDF_VALUE$ table) as the first bind value and the RDF term string as the second bind value. The value ID for a bound-in RDF term is required for performance reasons. The typical workflow would be to look up the value ID for an RDF term from the MDSYS.RDF_VALUE$ table (or with SEM_APIS.RES2VID) and then bind the ID and RDF term into the translated SQL.

Multiple query variables can be transformed into bind variables in a single query. In such cases, bind variables in the SQL translation will appear in the same order as the SPARQL BIND clauses appear in the SPARQL query string. That is, the (id, term) pair for the first BIND clause should be bound first, and the (id, term) pair for the second BIND clause should be bound second.

The following example shows the use of bind variables for SEM_APIS.SPARQL_TO_SQL from a PL/SQL block. A dummy bind variable ?n is declared..

DECLARE
  sparql_stmt clob;
  sql_stmt    clob;
  cur         sys_refcursor;
  vid         number;
  term        varchar2(4000);
  c_val       varchar2(4000);
BEGIN
  -- Add a dummy bind clause in the SPARQL statement
  sparql_stmt := 'SELECT ?c WHERE { 
                  BIND("" as ?s)
                  ?s :parentOf ?c }';
  -- Get the SQL translation for SPARQL statement
  sql_stmt := sem_apis.sparql_to_sql(
                sparql_stmt,
                sem_models('family'),
                SEM_Rulebases('RDFS','family_rb'),
                SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
                null,' USE_BIND_VAR=PLSQL PLUS_RDFT=VC ');

  -- execute with <http://www.example.org/family/Martha>
  term := '<http://www.example.org/family/Martha>';
  vid := sem_apis.res2vid('MDSYS.RDF_VALUE$',term);

  dbms_output.put_line(chr(10)||'?s='||term);
  open cur for 'select c$rdfterm from('|| sql_stmt || ')' using vid,term;
  loop
    fetch cur into c_val;
    exit when cur%NOTFOUND;
    dbms_output.put_line('|-->?c='||c_val);
  end loop;
  close cur;

  -- execute with <http://www.example.org/family/Sammy>
  term := '<http://www.example.org/family/Sammy>';
  vid := sem_apis.res2vid('MDSYS.RDF_VALUE$',term);

  dbms_output.put_line(chr(10)||'?s='||term);
  open cur for 'select c$rdfterm from('|| sql_stmt || ')' using vid,term;
  loop
    fetch cur into c_val;
    exit when cur%NOTFOUND;
    dbms_output.put_line('|-->?c='||c_val);
  end loop;
  close cur;

END;
/

The following example the use of bind variables from Java forSEM_APIS.SPARQL_TO_SQL. In this case, the hint USE_BIND_VAR=JDBC is used.

public static void sparqlToSqlTest() {

    try {
        // Get connection
        Connection conn=DriverManager.getConnection( 
                "jdbc:oracle:thin:@localhost:1521:orcl","testuser","testuser");          

        String sparqlStmt =
            "SELECT ?c WHERE {  \n" +
            "  BIND(\"\" as ?s) \n" +
            "  ?s :parentOf ?c      \n" +
            "}";

        // Get SQL translation of SPARQL statement
        // through sem_apis.sparql_to_sql
        OracleCallableStatement ocs = (OracleCallableStatement)conn.prepareCall(
            "begin" +
            "  ? := " +
            "    sem_apis.sparql_to_sql('" +
            "      "+sparqlStmt+"'," +
            "      sem_models('family')," +
            "      SEM_Rulebases('RDFS','family_rb')," +
            "      SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),null," +
            "  ' USE_BIND_VAR=JDBC PLUS_RDFT=VC " +
            " ',null,null);" +
            "end;");          
        ocs.registerOutParameter(1,Types.VARCHAR);
        ocs.execute();
        String sqlStmt = ocs.getString(1);
        ocs.close();

        // Setup statement to lookup value ids
        OracleCallableStatement ocsVid = (OracleCallableStatement)conn.prepareCall(
            "begin" +
            "  ? := sem_apis.res2vid(?,?);" +
            "end;");          

        // Execute SQL setting values for a bind variable
        PreparedStatement stmt=conn.prepareStatement(sqlStmt);

        // lookup value id for first value
        long valueId = 0;
        String term = "<http://www.example.org/family/Martha>";
        ocsVid.registerOutParameter(1,Types.NUMERIC);
        ocsVid.setString(2,"MDSYS.RDF_VALUE$");
        ocsVid.setString(3,term);
        ocsVid.execute();
        valueId = ocsVid.getLong(1);

        stmt.setLong(1, valueId);
        stmt.setString(2, term);
        ResultSet rs=stmt.executeQuery();

        // Print results
        System.out.println("\n?s="+term);
        while(rs.next()) {
            System.out.println("|-->?c=" + rs.getString("c$rdfterm"));
        }
        rs.close();

        // execute the same query for a different URI
        // lookup value id for next value
        valueId = 0;
        term = "<http://www.example.org/family/Sammy>";
        ocsVid.registerOutParameter(1,Types.NUMERIC);
        ocsVid.setString(2,"MDSYS.RDF_VALUE$");
        ocsVid.setString(3,term);
        ocsVid.execute();
        valueId = ocsVid.getLong(1);

        stmt.setLong(1, valueId);
        stmt.setString(2, term);
        rs=stmt.executeQuery();

        // Print results
        System.out.println("\n?s="+term);
        while(rs.next()) {
            System.out.println("|-->?c=" + rs.getString("c$rdfterm"));
        }
        rs.close();

        stmt.close();
        ocsVid.close();
        conn.close(); 

    } catch (SQLException e) {
        e.printStackTrace();
    }
}

Parent topic: Using the SEM_APIS.SPARQL_TO_SQL Function to Query Semantic Data

1.7.2 SEM_MATCH and SEM_APIS.SPARQL_TO_SQL Compared

The SEM_APIS.SPARQL_TO_SQL function avoids some limitations that are inherent in the SEM_MATCH table function due to its use of the rewritable table function interface. Specifically, SEM_APIS.SPARQL_TO_SQL adds the following capabilities.

SPARQL query string arguments larger than 4000 bytes (32K bytes with long varchar support) can be used.
The plain SQL returned from SEM_APIS.SPARQL_TO_SQL can be executed against read-only databases.
The plain SQL returned from SEM_APIS.SPARQL_TO_SQL can support PL/SQL and JDBC bind variables.

SEM_MATCH, however, provides some unique capabilities that are not possible with SEM_APIS.SPARQL_TO_SQL..

Support for projection optimization: If only the VAR$RDFVID column of a projected variable is selected from the SEM_MATCH invocation, the RDF_VALUE$ join for this variable will be avoided.
Support for advanced features that require the procedural start-fetch-close table function execution: SERVICE_JPDWN=T and OVERLOADED_NL=T options with SPARQL SERVICE.
The ability to execute queries interactively with tools like SQL*Plus.

Parent topic: Using the SEM_APIS.SPARQL_TO_SQL Function to Query Semantic Data

1.8 Loading and Exporting Semantic Data

You can load semantic data into a model in the database and export that data from the database into a staging table.

To load semantic data into a model, use one or more of the following options:

Bulk load or append data into the semantic data store from a staging table, with each row containing the three components -- subject, predicate, and object -- of an RDF triple and optionally a named graph. This is explained in Bulk Loading Semantic Data Using a Staging Table.

This is the fastest option for loading large amounts of data; however, it cannot handle triples containing object values with more than 4000 bytes.
Batch load using a Java client interface to load or append data from an N-Triple format file into the semantic data store (see Batch Loading N-Triple Format Semantic Data Using the Java API).

This option is slower than bulk loading, but it handles triples containing object values with more than 4000 bytes. However, this option does not handle named graphs.
Load into the application table using SQL INSERT statements that call the SDO_RDF_TRIPLE_S constructor, which results in the corresponding RDF triple, possibly including a graph name, to be inserted into the semantic data store, as explained in Loading Semantic Data Using INSERT Statements.

This option is convenient for loading small amounts of data.

To export semantic data, that is, to retrieve semantic data from Oracle Database where the results are in N-Triple or N-Quad format that can be stored in a staging table, use the SQL queries described in Exporting Semantic Data.

Note:

Effective with Oracle Database Release 12.1, you can export and import a semantic network using the full database export and import features of the Oracle Data Pump utility, as explained in Exporting or Importing a Semantic Network Using Oracle Data Pump.

Parent topic: RDF Semantic Graph Overview

1.8.1 Bulk Loading Semantic Data Using a Staging Table

You can load semantic data (and optionally associated non-semantic data) in bulk using a staging table. Call the SEM_APIS.LOAD_INTO_STAGING_TABLE procedure (described in SEM_APIS Package Subprograms) to load the data, and you can have during the load operation to check for syntax correctness. Then, you can call the SEM_APIS.BULK_LOAD_FROM_STAGING_TABLE procedure to load the data into the semantic store from the staging table. (If the data was not parsed during the load operation into the staging table, you must specify the PARSE keyword in the flags parameter when you call the SEM_APIS.BULK_LOAD_FROM_STAGING_TABLE procedure.)

The following example shows the format for the staging table, including all required columns and the required names for these columns, plus the optional RDF$STC_graph column which must be included if one or more of the RDF triples to be loaded include a graph name:

CREATE TABLE stage_table (
                     RDF$STC_sub varchar2(4000) not null,
                     RDF$STC_pred varchar2(4000) not null,
                     RDF$STC_obj varchar2(4000) not null,
                     RDF$STC_graph varchar2(4000)
);

If you also want to load non-semantic data, specify additional columns for the non-semantic data in the CREATE TABLE statement. The non-semantic column names must be different from the names of the required columns. The following example creates the staging table with two additional columns (SOURCE and ID) for non-semantic attributes.

CREATE TABLE stage_table_with_extra_cols (
                     source VARCHAR2(4000),
                     id NUMBER,
                     RDF$STC_sub varchar2(4000) not null,
                     RDF$STC_pred varchar2(4000) not null,
                     RDF$STC_obj varchar2(4000) not null,
                     RDF$STC_graph varchar2(4000)
);

Note:

For either form of the CREATE TABLE statement, you may want to add the COMPRESS clause to use table compression, which will reduce the disk space requirements and may improve bulk-load performance.

Both the invoker and the MDSYS user must have the following privileges: SELECT privilege on the staging table, and INSERT privilege on the application table.

1.8.1.1 Loading the Staging Table

You can load semantic data into the staging table, as a preparation for loading it into the semantic store, in several ways. Some of the common ways are the following:

Parent topic: Bulk Loading Semantic Data Using a Staging Table

1.8.1.1.1 Loading N-Triple Format Data into a Staging Table Using SQL*Loader

You can use the SQL*Loader utility to parse and load semantic data into a staging table. If you installed the demo files from the Oracle Database Examples media (see Oracle Database Examples Installation Guide), a sample control file is available at $ORACLE_HOME/md/demo/network/rdf_demos/bulkload.ctl. You can modify and use this file if the input data is in N-Triple format.

Objects longer than 4000 bytes cannot be loaded. If you use the sample SQL*Loader control file, triples (rows) containing such long values will be automatically rejected and stored in a SQL*Loader "bad" file. However, you can load these rejected rows by inserting them into the application table using SQL INSERT statements (see Loading Semantic Data Using INSERT Statements).

Parent topic: Loading the Staging Table

1.8.1.1.2 Loading N-Quad Format Data into a Staging Table Using an External Table

You can use an Oracle external table to load N-Quad format data (extended triple having four components) into a staging table, as follows:

Call the SEM_APIS.CREATE_SOURCE_EXTERNAL_TABLE procedure to create an external table, and then use the SQL STATEMENT ALTER TABLE to alter the external table to include the relevant input file name or names. You must have READ and WRITE privileges for the directory object associated with folder containing the input file or files.
After you create the external table, grant the MDSYS user SELECT and INSERT privileges on the table.
Call the SEM_APIS.LOAD_INTO_STAGING_TABLE procedure to populate the staging table.
After the loading is finished, issue a COMMIT statement to complete the transaction.

Example 1-91 Using an External Table to Load a Staging Table

-- Create a source external table (note: table names are case sensitive)
BEGIN
  sem_apis.create_source_external_table(
    source_table    => 'stage_table_source'
   ,def_directory   => 'DATA_DIR'
   ,bad_file        => 'CLOBrows.bad'
   );
END;
/
grant SELECT on "stage_table_source" to MDSYS;
 
-- Use ALTER TABLE to target the appropriate file(s)
alter table "stage_table_source" location ('demo_datafile.nt');
 
-- Load the staging table (note: table names are case sensitive)
BEGIN
  sem_apis.load_into_staging_table(
    staging_table => 'STAGE_TABLE'
   ,source_table  => 'stage_table_source'
   ,input_format  => 'N-QUAD');
END;
/

Rows where the objects and graph URIs (combined) are longer than 4000 bytes will be rejected and stored in a "bad" file. However, you can load these rejected rows by inserting them into the application table using SQL INSERT statements (see Loading Semantic Data Using INSERT Statements).

Example 1-91 shows the use of an external table to load a staging table.

Parent topic: Loading the Staging Table

1.8.1.2 Recording Event Traces During Bulk Loading

If a table named RDF$ET_TAB exists in the invoker's schema and if the MDSYS user has been granted the INSERT and UPDATE privileges on this table, event traces for some of the tasks performed during executions of the SEM_APIS.BULK_LOAD_FROM_STAGING_TABLE procedure will be added to the table. You may find the content of this table useful if you ever need to report any problems in bulk load. The RDF$ET_TAB table must be created as follows:

CREATE TABLE RDF$ET_TAB (
  proc_sid VARCHAR2(30), 
  proc_sig VARCHAR2(200),
  event_name varchar2(200),
  start_time timestamp,
  end_time timestamp,
  start_comment varchar2(1000) DEFAULT NULL,
  end_comment varchar2(1000) DEFAULT NULL
);
GRANT INSERT, UPDATE on RDF$ET_TAB to MDSYS;

Parent topic: Bulk Loading Semantic Data Using a Staging Table

1.8.2 Batch Loading N-Triple Format Semantic Data Using the Java API

Note:

The Java class oracle.spatial.rdf.client.BatchLoader described in this section has been deprecated, and it does not support loading of N-Quad data.

You are instead encouraged to use the bulk loading capabilities of the RDF Semantic Graph support for Apache Jena, as described in Bulk Loading Using RDF Semantic Graph Support for Apache Jena.

You can perform a batch (bulk) load operation for N-Triple format semantic data using the Java class oracle.spatial.rdf.client.BatchLoader, which is packaged in <ORACLE_HOME>/md/jlib/sdordf.jar. Before performing a batch load operation, ensure that the following are true:

The semantic data is in N-Triple format. (Several tools are available for converting RDF/XML to N-Triple format; see the Oracle Technology Network or perform a Web search for information about RDF/XML to N-Triple conversion.)
Oracle Database Release 11 or later, with Oracle Spatial and Graph, is installed, and partitioning is enabled.
A semantic technologies network, an application table, and its corresponding semantic model have been created in the database.
The CLASSPATH definition includes ojdbc6.jar.
You are using JDK version 1.5 or later. (You can use the Java version packaged under <ORACLE_HOME>/jdk/bin.)

To run the oracle.spatial.rdf.client.BatchLoader class, use a command (on a single command line) in the following general form (replacing the sample example database connection information with your own connection information).

Linux systems:

java -Ddb.user=scott -Ddb.password=password -Ddb.host=127.0.0.1 -Ddb.port=1522 -Ddb.sid=orcl -classpath ${ORACLE_HOME}/md/jlib/sdordf.jar:${ORACLE_HOME}/jdbc/lib/ojdbc6.jar oracle.spatial.rdf.client.BatchLoader <N-TripleFile> <tablename> <tablespaceName> <modelName>

Windows systems:

java -Ddb.user=scott -Ddb.password=password -Ddb.host=127.0.0.1 -Ddb.port=1522 -Ddb.sid=orcl -classpath %ORACLE_HOME%\md\jlib\sdordf.jar;%ORACLE_HOME%\jdbc\lib\ojdbc6.jar oracle.spatial.rdf.client.BatchLoader <N-TripleFile> <tablename> <tablespaceName> <modelName>

The values for -Ddb.user and -Ddb.password must correspond either to the owner of the model <modelName> or to a DBA user.

By default, BatchLoader assumes there are at least two columns, a column named ID of type NUMBER and a column named TRIPLE of type SDO_RDF_TRIPLE_S, in the user's application table. However, you can override the default names by using the JVM properties -DidColumn=<idColumnName> and -DtripleColumn=<tripleColumnName>. The ID column is not required; and to prevent BatchLoader from generating a sequence-like identifier in the ID column for each triple inserted, specify the JVM property -DjustTriple=true.

If the application table is not empty and if you want the batch loading to be done in append mode, specify an additional JVM property: -Dappend=true. Moreover, in append mode you might want to choose a different starting value for ID column in user's application table, and to accomplish this you can add the JVM property -DstartID=<startingIntegerValue> to the command line. By default, the ID column starts at 1 and is increased sequentially as new triples are inserted into the application table.

To skip the first n triples in <N-TripleFile>, add the JVM property -Dskip=<numberOfTriplesSkipped> to the command line.

To load an N-Triple file with a character set different from the default, specify the JVM property -Dcharset=<charsetName>. For example, -Dcharset="UTF-8" will recognize UTF-8 encoding. However, for UTF-8 characters to be stored properly in the N-Triple file, the Oracle database must be configured to use a corresponding universal character set, such as AL32UTF8.

The BatchLoader class supports loading an N-Triple file in compressed format. If the <N-TripleFile> has a file extension of .zip or .jar, the file will be uncompressed and loaded at the same time.

Parent topic: Loading and Exporting Semantic Data

1.8.3 Loading Semantic Data Using INSERT Statements

To load semantic data using INSERT statements, the data should be encoded using < > (angle brackets) for URIs, _: (underscore colon) for blank nodes, and " " (quotation marks) for literals. Spaces are not allowed in URIs or blank nodes. Use the SDO_RDF_TRIPLE_S constructor to insert the data, as described in Constructors for Inserting Triples. You must have INSERT privilege on the application table.

Note:

If URIs are not encoded with < > and literals with " ", statements will still be processed. However, the statements will take longer to load, since they will have to be further processed to determine their VALUE_TYPE values.

The following example includes statements with URIs, a blank node, a literal, a literal with a language tag, and a typed literal:

INSERT INTO nsu_data VALUES (SDO_RDF_TRIPLE_S('nsu', '<http://nature.example.com/nsu/rss.rdf>',
  '<http://purl.org/rss/1.0/title>', '"Nature''s Science Update"'));
INSERT INTO nsu_data VALUES (SDO_RDF_TRIPLE_S('nsu', '_:BNSEQN1001A',
  '<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>', 
  '<http://www.w3.org/1999/02/22-rdf-syntax-ns#Seq>'));
INSERT INTO nsu_data VALUES (SDO_RDF_TRIPLE_S('nsu',
  '<http://nature.example.com/cgi-taf/dynapage.taf?file=/nature/journal/v428/n6978/index.html>',
  '<http://purl.org/dc/elements/1.1/language>', '"English"@en-GB'));
INSERT INTO nature VALUES (SDO_RDF_TRIPLE_S('nsu', '<http://dx.doi.org/10.1038/428004b>',
  '<http://purl.org/dc/elements/1.1/date>', '"2004-03-04"^^xsd:date'));

To convert semantic XML data to INSERT statements, you can edit the sample rss2insert.xsl XSLT file to convert all the features in the semantic data XML file. The blank node constructor is used to insert statements with blank nodes. After editing the XSLT, download the Xalan XSLT processor (http://xml.apache.org/xalan-j/) and follow the installation instructions. To convert a semantic data XML file to INSERT statements using your edited version of the rss2insert.xsl file, use a command in the following format:

java org.apache.xalan.xslt.Process –in input.rdf -xsl rss2insert.xsl –out output.nt

Loading Data into Named Graphs Using INSERT Statements

Parent topic: Loading and Exporting Semantic Data

1.8.3.1 Loading Data into Named Graphs Using INSERT Statements

To load an RDF triple with a non-null graph name using an INSERT statement, you must append the graph name, enclosed within angle brackets (< >), after the model name and colon (:) separator character, as shown in the following example:

INSERT INTO articles_rdf_data VALUES ( 
  SDO_RDF_TRIPLE_S ('articles:<http://examples.com/ns#Graph1>',
    '<http://nature.example.com/Article101>',
    '<http://purl.org/dc/elements/1.1/creator>',
    '"John Smith"'));

Parent topic: Loading Semantic Data Using INSERT Statements

1.8.4 Exporting Semantic Data

This section contains the following topics related to exporting semantic data, that is, retrieving semantic data from Oracle Database where the results are in N-Triple or N-Quad format that can be stored in a staging table.

Parent topic: Loading and Exporting Semantic Data

1.8.4.1 Retrieving Semantic Data from an Application Table

Semantic data can be retrieved from an application table using the member functions of SDO_RDF_TRIPLE_S, as shown in Example 1-92 (where the output is reformatted for readability).

Example 1-92 Retrieving Semantic Data from an Application Table

--
-- Retrieves model-graph, subject, predicate, and object
--
SQL> SELECT a.triple.GET_MODEL() AS model_graph, a.triple.GET_SUBJECT() AS sub, a.triple.GET_PROPERTY() pred, a.triple.GET_OBJECT() obj FROM articles_rdf_data a;

MODEL_GRAPH
--------------------------------------------------------------------------------
SUB
--------------------------------------------------------------------------------
PRED
--------------------------------------------------------------------------------
OBJ
--------------------------------------------------------------------------------
ARTICLES
<http://nature.example.com/Article1>
<http://purl.org/dc/elements/1.1/title>
"All about XYZ"


ARTICLES
<http://nature.example.com/Article1>
<http://purl.org/dc/elements/1.1/creator>
"Jane Smith"


ARTICLES
<http://nature.example.com/Article1>
<http://purl.org/dc/terms/references>
<http://nature.example.com/Article2>


ARTICLES
<http://nature.example.com/Article1>
<http://purl.org/dc/terms/references>
<http://nature.example.com/Article3>


ARTICLES
<http://nature.example.com/Article2>
<http://purl.org/dc/elements/1.1/title>
"A review of ABC"


ARTICLES
<http://nature.example.com/Article2>
<http://purl.org/dc/elements/1.1/creator>
"Joe Bloggs"


ARTICLES
<http://nature.example.com/Article2>
<http://purl.org/dc/terms/references>
<http://nature.example.com/Article3>


7 rows selected.

Parent topic: Exporting Semantic Data

1.8.4.2 Retrieving Semantic Data from an RDF Model

Semantic data can be retrieved from an RDF model using the SEM_MATCH table function (described in Using the SEM_MATCH Table Function to Query Semantic Data), as shown in Example 1-93.

Example 1-93 Retrieving Semantic Data from an RDF Model

--
-- Retrieves graph, subject, predicate, and object
--
SQL> select to_char(g$rdfterm) graph, to_char(x$rdfterm) sub, to_char(p$rdfterm) pred, y$rdfterm obj from table(sem_match('Select ?g ?x ?p ?y FROM NAMED <http://examples.com/ns#Graph1> {GRAPH ?g {?x ?p ?y}}',sem_models('articles'),null,null,null,null,' GRAPH_MATCH_UNNAMED=T PLUS_RDFT=T '));
 
GRAPH
------------------------------------------------------------
SUB
------------------------------------------------------------
PRED
------------------------------------------------------------
OBJ
---------------------------------------------------------------------------
<http://examples.com/ns#Graph1>
_:m99g3C687474703A2F2F6578616D706C65732E636F6D2F6E73234772617068313Egmb2
<http://purl.org/dc/elements/1.1/creator>
_:m99g3C687474703A2F2F6578616D706C65732E636F6D2F6E73234772617068313Egmb1
 
<http://examples.com/ns#Graph1>
<http://nature.example.com/Article102>
<http://purl.org/dc/elements/1.1/creator>
_:m99g3C687474703A2F2F6578616D706C65732E636F6D2F6E73234772617068313Egmb1
 
<http://examples.com/ns#Graph1>
<http://nature.example.com/Article101>
<http://purl.org/dc/elements/1.1/creator>
"John Smith"
 
<http://nature.example.com/Article1>
<http://purl.org/dc/elements/1.1/creator>
"Jane Smith"

Parent topic: Exporting Semantic Data

1.8.4.3 Removing Model and Graph Information from Retrieved Blank Node Identifiers

Blank node identifiers retrieved during the retrieval of semantic data can be trimmed to remove the occurrence of model and graph information using the transformations shown in the code excerpt in Example 1-94, which are applicable to VARCHAR2 (for example, subject component) and CLOB (for example, object component) data, respectively.

Example 1-95 shows the results obtained after using these two transformations in Example 1-94 on the sub and obj columns, respectively, using the semantic data retrieval query described in Retrieving Semantic Data from an RDF Model.

Example 1-94 Retrieving Semantic Data from an Application Table

--
-- Transformation on column "sub VARCHAR2" 
-- holding blank node identifier values
--
Select (case substr(sub,1,2) when '_:' then '_:' || substr(sub,instr(sub,'m',1,2)+1) else sub end) from …
--
-- Transformation on column "obj CLOB" 
-- holding blank node identifier values
--
Select (case dbms_lob.substr(obj,2,1) when '_:' then to_clob('_:' || substr(obj,instr(obj,'m',1,2)+1)) else obj end) from …

Example 1-95 Results from Applying Transformations from Example 1-94

--
-- Results obtained by applying transformations on the sub and pred cols
-- 
SQL> select (case substr(sub,1,2) when '_:' then '_:' || substr(sub,instr(sub,'m',1,2)+1) else sub end) sub, pred, (case dbms_lob.substr(obj,2,1) when '_:' then to_clob('_:' || substr(obj,instr(obj,'m',1,2)+1)) else obj end) obj from (select to_char(g$rdfterm) graph, to_char(x$rdfterm) sub, to_char(p$rdfterm) pred, y$rdfterm obj from table(sem_match('Select ?g ?x ?p ?y FROM NAMED <http://examples.com/ns#Graph1> {GRAPH ?g {?x ?p ?y}}',sem_models('articles'),null,null,null,null,' GRAPH_MATCH_UNNAMED=T PLUS_RDFT=T ')));
 
SUB
------------------------------------------------------------
PRED
------------------------------------------------------------
OBJ
---------------------------------------------------------------------------
_:b2
<http://purl.org/dc/elements/1.1/creator>
_:b1
 
<http://nature.example.com/Article102>
<http://purl.org/dc/elements/1.1/creator>
_:b1

Parent topic: Exporting Semantic Data

1.8.5 Exporting or Importing a Semantic Network Using Oracle Data Pump

Effective with Oracle Database Release 12.1, you can export and import a semantic network using the full database export and import features of the Oracle Data Pump utility. The network is moved as part of the full database export or import, where the whole database is represented in an Oracle dump (.dmp) file.

The following usage notes apply to using Data Pump to export or import a semantic network:

The target database for an import must have the RDF Semantic Graph software installed, and there cannot be a pre-existing semantic network.
Semantic networks using fine-grained access control (triple-level or resource-level OLS or VPD) cannot be exported or imported.
Semantic document indexes for SEM_CONTAINS (MDSYS.SEMCONTEXT index type) and semantic indexes for SEM_RELATED (MDSYS.SEM_INDEXTYPE index type) must be dropped before an export and re-created after an import.
Only default privileges for semantic network objects (those that exist just after object creation) are preserved during export and import. For example, if user A creates semantic model M and grants SELECT on MDSYS.RDFM_M to user B, only user A's SELECT privilege on MDSYS.RDFM_M will be present after the import. User B will not have SELECT privilege on MDSYS.RDFM_M after the import. Instead, user B's SELECT privilege will have to be granted again.
The Data Pump command line option transform=oid:n must be used when exporting or importing semantic network data. For example, use a command in the following format:
```
impdp system/<password-for-system> directory=dpump_dir dumpfile=rdf.dmp full=YES version=12 transform=oid:n
```

For Data Pump usage information and examples, see the relevant chapters in Part I of Oracle Database Utilities.

Parent topic: Loading and Exporting Semantic Data

1.8.6 Purging Unused Values

Deletion of triples over time may lead to a subset of the values in the RDF_VALUE$ table becoming unused in any of the RDF triples or rules currently in the semantic network. If the count of such unused values becomes large and a significant portion of the RDF_VALUE$ table, you may want to purge the unused values using the SEM_APIS.PURGE_UNUSED_VALUES subprogram.

Before the purging, MDSYS must be granted SELECT privilege on application tables for all the RDF models. This can be done directly using the GRANT command or by using the SEM_APIS.PRIVILEGE_ON_APP_TABLES subprogram.

Event traces for tasks performed during the purge operation may be recorded into the RDF$ET_TAB table, if present in the invoker's schema, as described in Recording Event Traces During Bulk Loading.

The following example purges unused values from the RDF_VALUE$ table. The example does not consider named graphs or CLOBs. It also assumes that the data from the example in Example: Journal Article Information has been loaded.

Example 1-96 Purging Unused Values

-- Purging unused values
set numwidth 20

-- Create view to show the values actually used in the RDF model
CREATE VIEW values_used_in_model (value_id) as
  SELECT a.triple.rdf_s_id FROM articles_rdf_data a UNION
  SELECT a.triple.rdf_p_id FROM articles_rdf_data a UNION
  SELECT a.triple.rdf_c_id FROM articles_rdf_data a UNION
  SELECT a.triple.rdf_o_id FROM articles_rdf_data a;
 
View created.

-- Create views to show triples in the model
CREATE VIEW triples_in_app_table as
  SELECT a.triple.GET_SUBJECT() AS s, a.triple.GET_PROPERTY() AS p, a.triple.GET_OBJ_VALUE() AS o
    FROM articles_rdf_data a;
 
View created.

CREATE VIEW triples_in_rdf_model as
  SELECT s, p, o FROM TABLE ( SEM_MATCH('{?s ?p ?o}', SEM_MODELS('articles'), null, null, null ));
 
View created.

--
-- Content before deletion
--

-- Values in mdsys.RDF_VALUE$
CREATE TABLE values_before_deletion as select value_id from mdsys.rdf_value$;
 
Table created.

-- Values used in the RDF model
CREATE TABLE used_values_before_deletion as
  SELECT * FROM values_used_in_model;
 
Table created.

-- Content of RDF model
CREATE TABLE atab_triples_before_deletion
  as select * from triples_in_app_table;
 
Table created.

CREATE TABLE model_triples_before_deletion
  as select * from triples_in_rdf_model;
 
Table created.

-- Delete some triples so that some of the values become unused
DELETE FROM articles_rdf_data a
   WHERE a.triple.GET_PROPERTY() = '<http://purl.org/dc/elements/1.1/title>'
     OR a.triple.GET_SUBJECT() = '<http://nature.example.com/Article1>';
 
5 rows deleted.

-- Content of RDF model after deletion
CREATE TABLE atab_triples_after_deletion
  as select * from triples_in_app_table;
 
Table created.

CREATE TABLE model_triples_after_deletion
  as select * from triples_in_rdf_model;
 
Table created.

-- Values that became unused in the RDF model
SELECT * from used_values_before_deletion
  MINUS
  SELECT * FROM values_used_in_model;
 
VALUE_ID
--------------------
 1399113999628774496
 4597469165946334122
 6345024408674005890
 7299961478807817799
 7995347759607176041

-- RDF_VALUE$ content, however, is unchanged
SELECT value_id from values_before_deletion
  MINUS
  select value_id from mdsys.rdf_value$;
 
no rows selected

-- Now purge the values from RDF_VALUE$ (requires that MDSYS has
-- SELECT privilege on *all* the app tables in the semantic network)
EXECUTE sem_apis.privilege_on_app_tables;
 
PL/SQL procedure successfully completed.
 
EXECUTE sem_apis.purge_unused_values;
 
PL/SQL procedure successfully completed.

-- RDF_VALUE$ content is NOW changed due to the purge of unused values
SELECT value_id from values_before_deletion
  MINUS
  select value_id from mdsys.rdf_value$;
 
VALUE_ID
--------------------
 1399113999628774496
 4597469165946334122
 6345024408674005890
 7299961478807817799
 7995347759607176041

-- Content of RDF model after purge
CREATE TABLE atab_triples_after_purge
  as select * from triples_in_app_table;
 
Table created.

CREATE TABLE model_triples_after_purge
  as select * from triples_in_rdf_model;
 
Table created.

-- Compare triples present before purging of values and after purging
SELECT * from atab_triples_after_deletion
  MINUS
  SELECT * FROM atab_triples_after_purge;
 
no rows selected

SELECT * from model_triples_after_deletion
  MINUS
  SELECT * FROM model_triples_after_purge;
 
no rows selected

Parent topic: Loading and Exporting Semantic Data

1.9 Using Semantic Network Indexes

Semantic network indexes are nonunique B-tree indexes that you can add, alter, and drop for use with models and entailments in a semantic network.

You can use such indexes to tune the performance of SEM_MATCH queries on the models and entailments in the network. As with any indexes, semantic network indexes enable index-based access that suits your query workload. This can lead to substantial performance benefits, such as in the following example scenarios:

If your graph pattern is '{<John> ?p <Mary>}', you may want to have a usable 'CSP' or 'SCP' index for the target model or models and on the corresponding entailment, if used in the query.
If your graph pattern is '{?x <talksTo> ?y . ?z ?p ?y}', you may want to have a usable semantic network index on the relevant model or models and entailment, with C as the leading key (for example, 'C' or 'CPS').

However, using semantic network indexes can affect overall performance by increasing the time required for DML, load, and inference operations.

You can create and manage semantic network indexes using the following subprograms:

All of these subprograms have an index_code parameter, which can contain any sequence of the following letters (without repetition): P, C, S, G, M. These letters used in the index_code correspond to the following columns in the SEMM_* and SEMI_* views: P_VALUE_ID, CANON_END_NODE_ID, START_NODE_ID, G_ID, and MODEL_ID.

The SEM_APIS.ADD_SEM_INDEX procedure creates a semantic network index that results in creation of a nonunique B-tree index in UNUSABLE status for each of the existing models and entailments. The name of the index is RDF_LNK_<index_code>_IDX and the index is owned by MDSYS. This operation is allowed only if the invoker has DBA role. The following example shows creation of the PSCGM index with the following key: <P_VALUE_ID, START_NODE_ID, CANON_END_NODE_ID, G_ID, MODEL_ID>.

EXECUTE SEM_APIS.ADD_SEM_INDEX('PSCGM');

After you create a semantic network index, each of the corresponding nonunique B-tree indexes is in the UNUSABLE status, because making it usable can cause significant time and resources to be used, and because subsequent index maintenance operations might involve performance costs that you do not want to incur. You can make a semantic network index usable or unusable for specific models or entailments that you own by calling the SEM_APIS.ALTER_SEM_INDEX_ON_MODEL and SEM_APIS.ALTER_SEM_INDEX_ON_ENTAILMENT procedures and specifying 'REBUILD' or 'UNUSABLE' as the command parameter. Thus, you can experiment by making different semantic network indexes usable and unusable, and checking for any differences in performance. For example, the following statement makes the PSCGM index usable for the FAMILY model:

EXECUTE SEM_APIS.ALTER_SEM_INDEX_ON_MODEL('FAMILY','PSCGM','REBUILD');

Also note the following:

Independent of any semantic network indexes that you create, when a semantic network is created, one of the indexes that is automatically created is an index that you can manage by referring to the index_code as 'PSCGM' when you call the subprograms mentioned in this section.
When you create a new model or a new entailment, a new nonunique B-tree index is created for each of the semantic network indexes, and each such B-tree index is in the USABLE status.
Including the MODEL_ID column in a semantic network index key (by including 'M' in the index_code value) may improve query performance. This is particularly relevant when virtual models are used.

MDSYS.SEM_NETWORK_INDEX_INFO View

Parent topic: RDF Semantic Graph Overview

1.9.1 MDSYS.SEM_NETWORK_INDEX_INFO View

Information about all network indexes on models and entailments is maintained in the MDSYS.SEM_NETWORK_INDEX_INFO view, which includes (a partial list) the columns shown in Table 1-17 and one row for each network index.

Table 1-17 MDSYS.SEM_NETWORK_INDEX_INFO View Columns (Partial List)

Column Name	Data Type	Description
NAME	VARCHAR2(30)	Name of the RDF model or entailment
TYPE	VARCHAR2(10)	Type of object on which the index is built: `MODEL`, `ENTAILMENT`, or `NETWORK`
ID	NUMBER	ID number for the model or entailment, or zero (0) for an index on the network
INDEX_CODE	VARCHAR2(25)	Code for the index (for example, `PSCGM`).
INDEX_NAME	VARCHAR2(30)	Name of the index (for example, `RDF_LNK_PSCGM_IDX`)
LAST_REFRESH	TIMESTAMP(6) WITH TIME ZONE	Timestamp for the last time this content was refreshed

In addition to the columns listed in Table 1-17, the MDSYS.SEM_NETWORK_INDEX_INFO view contains columns from the ALL_INDEXES and ALL_IND_PARTITIONS views (both described in Oracle Database Reference), including:

From the ALL_INDEXES view: UNIQUENESS, COMPRESSION, PREFIX_LENGTH
From the ALL_IND_PARTITIONS view: STATUS, TABLESPACE_NAME, BLEVEL, LEAF_BLOCKS, NUM_ROWS, DISTINCT_KEYS, AVG_LEAF_BLOCKS_PER_KEY, AVG_DATA_BLOCKS_PER_KEY, CLUSTERING_FACTOR, SAMPLE_SIZE, LAST_ANALYZED

Note that the information in the MDSYS.SEM_NETWORK_INDEX_INFO view may sometimes be stale. You can refresh this information by using the SEM_APIS.REFRESH_SEM_NETWORK_INDEX_INFO procedure.

Parent topic: Using Semantic Network Indexes

1.10 Using Data Type Indexes

Data type indexes are indexes on the values of typed literals stored in a semantic network.

These indexes may significantly improve the performance of SEM_MATCH queries involving certain types of FILTER expressions. For example, a data type index on xsd:dateTime literals may speed up evaluation of the filter (?x < "1929-11-16T13:45:00Z"^^xsd:dateTime). Indexes can be created for several data types, which are listed in Table 1-18.

Table 1-18 Data Types for Data Type Indexing

Data Type URI	Oracle Type	Index Type
http://www.w3.org/2001/XMLSchema#decimal	NUMBER	Non-unique B-tree (creates a single index for all xsd numeric types, including `xsd:float`, `xsd:double`, and `xsd:decimal` and all of its subtypes)
http://www.w3.org/2001/XMLSchema#string	VARCHAR2	Non-unique B-tree (creates a single index for `xsd:string` typed literals and plain literals)
http://www.w3.org/2001/XMLSchema#time	TIMESTAMP WITH TIMEZONE	Non-unique B-tree
http://www.w3.org/2001/XMLSchema#date	TIMESTAMP WITH TIMEZONE	Non-unique B-tree
http://www.w3.org/2001/XMLSchema#dateTime	TIMESTAMP WITH TIMEZONE	Non-unique B-tree
http://xmlns.oracle.com/rdf/text	(Not applicable)	CTXSYS.CONTEXT
http://xmlns.oracle.com/rdf/geo/WKTLiteral	SDO_GEOMETRY	MDSYS.SPATIAL_INDEX
http://www.opengis.net/geosparql#wktLiteral	SDO_GEOMETRY	MDSYS.SPATIAL_INDEX
http://www.opengis.net/geosparql#gmlLiteral	SDO_GEOMETRY	MDSYS.SPATIAL_INDEX
http://xmlns.oracle.com/rdf/like	VARCHAR2	Non-unique B-tree

The suitability of data type indexes depends on your query workload. Data type indexes on xsd data types can be used for filters that compare a variable with a constant value, and are particularly useful when queries have an unselective graph pattern with a very selective filter condition. Appropriate data type indexes are required for queries with spatial or text filters.

While data type indexes improve query performance, overhead from incremental index maintenance can degrade the performance of DML and bulk load operations on the semantic network. For bulk load operations, it may often be faster to drop data type indexes, perform the bulk load, and then re-create the data type indexes.

You can add, alter, and drop data type indexes using the following procedures, which are described in SEM_APIS Package Subprograms:

Information about existing data type indexes is maintained in the MDSYS.SEM_DTYPE_INDEX_INFO view, which has the columns shown in Table 1-19 and one row for each data type index.

Table 1-19 MDSYS.SEM_DTYPE_INDEX_INFO View Columns

Column Name	Data Type	Description
DATATYPE	VARCHAR2(51)	Data type URI
INDEX_NAME	VARCHAR2(30)	Name of the index
STATUS	VARCHAR2(8)	Status of the index: `USABLE` or `UNUSABLE`
TABLESPACE_NAME	VARCHAR2(30)	Tablespace for the index
FUNCIDX_STATUS	VARCHAR2(8)	Status of the function-based index: `NULL`, `ENABLED`, or `DISABLED`

You can use the HINT0 hint to ensure that data type indexes are used during query evaluation, as shown in Example 1-97, which finds all grandfathers who were born before November 16, 1929.

Example 1-97 Using HINT0 to Ensure Use of Data Type Index

SELECT x, y
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male . ?x :birthDate ?bd
       FILTER (?bd <= "1929-11-15T23:59:59Z"^^xsd:dateTime) }',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null, null, 
    'HINT0={ LEADING(?bd) INDEX(?bd rdf_v$dateTime_idx) } 
             FAST_DATE_FILTER=T' ));

Parent topic: RDF Semantic Graph Overview

1.11 Managing Statistics for Semantic Models and the Semantic Network

Statistics are critical to the performance of SPARQL queries and OWL inference against semantic data stored in an Oracle database.

Oracle Database Release 11g introduced SEM_APIS.ANALYZE_MODEL, SEM_APIS.ANALYZE_ENTAILMENT, and SEM_PERF.GATHER_STATS to analyze semantic data and keep statistics up to date. These APIs are straightforward to use and they are targeted at regular users who may not care about the internal details about table and partition statistics.

You can export, import, set, and delete model and entailment statistics, and can export, import, and delete network statistics, using the following subprograms:

This section contains the following topics related to managing statistics for semantic models and the semantic network.

Parent topic: RDF Semantic Graph Overview

1.11.1 Saving Statistics at a Model Level

If queries and inference against an existing model are executed efficiently, as the owner of the model, you can save the statistics of the existing model.

-- Login as the model owner (for example, SCOTT)
-- Create a stats table. This is required.
execute dbms_stats.create_stat_table('scott','rdf_stat_tab');
 
-- You must grant access to MDSYS
SQL> grant select, insert, delete, update on scott.rdf_stat_tab to MDSYS;
 
-- Now export the statistics of model TEST
execute sdo_rdf.export_model_stats('TEST','rdf_stat_tab', 'model_stat_saved_on_AUG_10', true, 'SCOTT', 'OBJECT_STATS');

You can also save the statistics of an entailment (entailed graph) by using SEM_APIS.EXPORT_ENTAILMENT_STATS .

execute sem_apis.create_entailment('test_inf',sem_models('test'),sem_rulebases('owl2rl'),0,null);
PL/SQL procedure successfully completed.
 
execute sem_apis.export_entailment_stats('TEST_INF','rdf_stat_tab', 'inf_stat_saved_on_AUG_10', true, 'SCOTT', 'OBJECT_STATS');

Parent topic: Managing Statistics for Semantic Models and the Semantic Network

1.11.2 Restoring Statistics at a Model Level

As the owner of a model, can restore the statistics that were previously saved with SEM_APIS.EXPORT_MODEL_STATS . This may be necessary if updates have been applied to this model and statistics have been re-collected. A change in statistics might cause a plan change to existing SPARQL queries, and if such a plan change is undesirable, then an old set of statistics can be restored.

execute sem_apis.import_model_stats('TEST','rdf_stat_tab', 'model_stat_saved_on_AUG_10', true, 'SCOTT', false, true, 'OBJECT_STATS');

You can also restore the statistics of an entailment (entailed graph) by using SEM_APIS.IMPORT_ENTAILMENT_STATS .

execute sem_apis.import_entailment_stats('TEST','rdf_stat_tab', 'inf_stat_saved_on_AUG_10', true, 'SCOTT', false, true, 'OBJECT_STATS');

Parent topic: Managing Statistics for Semantic Models and the Semantic Network

1.11.3 Saving Statistics at the Network Level

You can save statistics at the network level.

-- First, create a user RDF_ADMIN and assign access to package SEM_PERF to RDF_ADMIN
--
-- As SYS
--
create user RDF_ADMIN identified by RDF_ADMIN;
 
grant connect, resource, unlimited tablespace to RDF_ADMIN;
 
grant execute on sem_perf to RDF_ADMIN;
 
conn RDF_ADMIN/<password>
 
execute dbms_stats.create_stat_table('RDF_ADMIN','rdf_stat_tab');
grant select, insert, delete, update on RDF_ADMIN.rdf_stat_tab to MDSYS;
 
--
-- This API call will save the statistics of both MDSYS.RDF_VALUE$ table
-- and MDSYS.RDF_LINK$
--
execute sem_perf.export_network_stats('rdf_stat_tab', 'NETWORK_ALL_saved_on_Aug_10', true, 'RDF_ADMIN', 'OBJECT_STATS');
 
--
-- Alternatively, you can save statistics of the MDSYS.RDF_VALUE$ table
--
execute sem_perf.export_network_stats('rdf_stat_tab', 'NETWORK_VALUE_TAB_saved_on_Aug_10', true, 'RDF_ADMIN', 'OBJECT_STATS', options=> mdsys.sdo_rdf.VALUE_TAB_ONLY);
 
--
-- Or, you can save statistics of the MDSYS.RDF_LINK$ table
--
execute sem_perf.export_network_stats('rdf_stat_tab', 'NETWORK_LINK_TAB_saved_on_Aug_10', true, 'RDF_ADMIN', 'OBJECT_STATS', options=> mdsys.sdo_rdf.LINK_TAB_ONLY);

Parent topic: Managing Statistics for Semantic Models and the Semantic Network

1.11.4 Restoring Statistics at the Network Level

The privileged user from Saving Statistics at the Network Level can restore the network level statistics using SEM_PERF.IMPORT_NETWORK_STATS .

conn RDF_ADMIN/<password>
 
execute sem_perf.import_network_stats('rdf_stat_tab', 'NETWORK_ALL_saved_on_Aug_10', true, 'RDF_ADMIN', false, true, 'OBJECT_STATS');

Parent topic: Managing Statistics for Semantic Models and the Semantic Network

1.11.5 Setting Statistics at a Model Level

As the owner of a model, you can manually adjust the statistics for this model. (However, before you adjust statistics, you should save the statistics first so that they can be restored if necessary.) The following example sets two metrics: number of rows and number of blocks for the model.

execute sem_apis.set_model_stats('TEST', numrows=>10, numblks=>1,no_invalidate=>false);

You can also set the statistics for the entailment by using SEM_APIS.SET_ENTAILMENT_STATS .

execute sem_apis.set_entailment_stats('TEST_INF', numrows=>10, numblks=>1,no_invalidate=>false);

Parent topic: Managing Statistics for Semantic Models and the Semantic Network

1.11.6 Deleting Statistics at a Model Level

Removing statistics can also have an impact on execution plans. As owner of a model, you can remove the statistics for the model.

execute sem_apis.delete_model_stats('TEST', no_invalidate=> false);

You can also remove the statistics for the entailment by using SEM_APIS.DELETE_ENTAILMENT_STATS. (However, before you remove statistics of a model or an entailment, you should save the statistics first so that they can be restored if necessary.)

execute sem_apis.delete_entailment_stats('TEST_INF', no_invalidate=> false);

Parent topic: Managing Statistics for Semantic Models and the Semantic Network

1.12 Support for SPARQL Update Operations on a Semantic Model

Effective with Oracle Database Release 12.2, you can perform SPARQL Update operations on a semantic model.

The W3C provides SPARQL 1.1 Update (https://www.w3.org/TR/2013/REC-sparql11-update-20130321/), an update language for RDF graphs. SPARQL 1.1 Update is supported in Oracle Database semantic technologies through the SEM_APIS.UPDATE_MODEL procedure.

Before performing any SPARQL Update operations on a model, some prerequisites apply:

The SEM_APIS.CREATE_SPARQL_UPDATE_TABLES procedure should be run in the schema of each user that will be using the SEM_APIS.UPDATE_MODEL procedure.
Each user that will update a model using the SEM_APIS.UPDATE_MODEL procedure must have the INSERT privilege on the application table associated with the apply_model model, and the MDSYS user must be granted the INSERT privilege on that table (for example, GRANT INSERT on APP_TAB1 to MDSYS;).

The application table is the table that holds references to the semantic data for the model.
To run a LOAD operation, the user must have the CREATE ANY DIRECTORY and DROP ANY DIRECTORY privileges.

Examples follow that show update operations being performed on an RDF model.

Example 1-98 INSERT DATA Operation

This example shows an INSERT DATA operation that inserts several triples in the default graph of the electronics model.

-- Dataset before operation:
#Empty default graph
-- Update operation:
BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/> 
    INSERT DATA {
       :camera1 :name "Camera 1" .
       :camera1 :price 120 .
       :camera1 :cameraType :Camera .
       :camera2 :name "Camera 2" .
       :camera2 :price 150 .
       :camera2 :cameraType :Camera .
      } ');
END;
/

-- Dataset after operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera1 :name "Camera 1";
         :price 120;
         :cameraType :Camera .
:camera2 :name "Camera 2"; 
         :price 150;
         :cameraType :Camera .

Example 1-99 DELETE DATA Operation

This example shows a DELETE DATA operation that removes a single triple from the default graph of the electronics model.

-- Dataset before operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera1 :name "Camera 1";
         :price 120;
         :cameraType :Camera .
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
-- Update operation:
BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/> 
    DELETE DATA { :camera1 :price 120 . } ');
END;
/

-- Dataset after operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera1 :name "Camera 1";
         :cameraType :Camera .
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .

Example 1-100 DELETE/INSERT Operation on Default Graph

This example performs a DELETE/INSERT operation. The :cameraType of :camera1 is updated to :digitalCamera.

-- Dataset before operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera1 :name "Camera 1";
         :cameraType :Camera .
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .

-- Update operation:
BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/> 
    DELETE { :camera1 :cameraType ?type . } 
    INSERT { :camera1 :cameraType :digitalCamera . } 
    WHERE  { :camera1 :cameraType ?type . }');
END;
/

-- Dataset after operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera1 :name "Camera 1";
         :cameraType :digitalCamera .
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .

Example 1-101 DELETE/INSERT Operation Involving Default Graph and Named Graph

Graphs can also be specified inside the DELETE and INSERT templates, as well as inside the WHERE clause. This example moves all triples corresponding to digital cameras from the default graph to the graph :digitalCameras.

-- Dataset before operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera1 :name "Camera 1";
         :cameraType :digitalCamera .
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Empty graph :digitalCameras

-- Update operation:
BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/> 
    DELETE { ?s ?p ?o } 
    INSERT { graph :digitalCameras { ?s ?p ?o } }
    WHERE  { ?s :cameraType :digitalCamera .
             ?s ?p ?o }');
END;
/

-- Dataset after operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Graph :digitalCameras
GRAPH :digitalCameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}

Example 1-102 INSERT WHERE and DELETE WHERE Operations

One of either the DELETE template or the INSERT template can be omitted from a DELETE/INSERT operation. In addition, the template following DELETE can be omitted as a shortcut for using the WHERE pattern as the DELETE template. This example uses an INSERT WHERE statement to insert the contents of the :digitalCameras graph to the :cameras graph, and it uses a DELETE WHERE statement (with syntactic shortcut) to delete all contents of the :cameras graph.


-- INSERT WHERE
-- Dataset before operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Graph :digitalCameras
GRAPH :digitalCameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}
#Empty graph :cameras

-- Update operation:
BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/> 
    INSERT { graph :cameras { ?s ?p ?o } }
    WHERE  { graph :digitalCameras { ?s ?p ?o } }');
END;
/

-- Dataset after operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Graph :digitalCameras
GRAPH :digitalCameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}
#Graph :cameras
GRAPH :cameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}

-- DELETE WHERE
-- Dataset before operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Graph :digitalCameras
GRAPH :digitalCameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}
#Graph :cameras
GRAPH :cameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}

-- Update operation:
BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/> 
    DELETE WHERE { graph :cameras { ?s ?p ?o } }');
END;
/

-- Dataset after operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Graph :digitalCameras
GRAPH :digitalCameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}
#Empty graph :cameras

Example 1-103 COPY Operation

This example performs a COPY operation. All data from the default graph is inserted into the graph :cameras. Existing data from :cameras, if any, is removed before the insertion.

-- Dataset before operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Graph :digitalCameras
GRAPH :digitalCameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}
#Graph :cameras
GRAPH :cameras {
  :camera3 :name "Camera 3" .
}

-- Update operation:
BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/>
    COPY DEFAULT TO GRAPH :cameras');
END;
/

-- Dataset after operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Graph :digitalCameras
GRAPH :digitalCameras {
  :camera1 :name "Camera 1"; 
           :cameraType :digitalCamera .
}
#Graph :cameras
GRAPH :cameras {
  :camera2 :name "Camera 2";
           :price 150;
           :cameraType :Camera .
}

Example 1-104 ADD Operation

This example adds all the triples in the graph :digitalCameras to the graph :cameras.

-- Dataset before operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Graph :digitalCameras
GRAPH :digitalCameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}
#Graph :cameras
GRAPH :cameras {
  :camera2 :name "Camera 2";
           :price 150;
           :cameraType :Camera .
}

-- Update operation:
BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/>
    ADD GRAPH :digitalCameras TO GRAPH :cameras');
END;
/

-- Dataset after operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Graph :digitalCameras
GRAPH :digitalCameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}
#Graph :cameras
GRAPH :cameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
  :camera2 :name "Camera 2";
           :price 150;
           :cameraType :Camera .
}

Example 1-105 MOVE Operation

This example moves all the triples in the graph :digitalCameras to the graph :digCam.

-- Dataset before operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Graph :digitalCameras
GRAPH :digitalCameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}
#Graph :cameras
GRAPH :cameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
  :camera2 :name "Camera 2";
           :price 150;
           :cameraType :Camera .
}
#Graph :digCam
GRAPH :digCam {
  :camera4 :cameraType :digCamera .
}

-- Update operation:
BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/>
    MOVE GRAPH :digitalCameras TO GRAPH :digCam');
END;
/

-- Dataset after operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2" .
         :camera2 :price 150 .
         :camera2 :cameraType :Camera .
#Empty graph :digitalCameras
#Graph :cameras
GRAPH :cameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
  :camera2 :name "Camera 2";
           :price 150;
           :cameraType :Camera .
}
#Graph :digCam
GRAPH :digCam {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}

Example 1-106 CLEAR Operation

This example performs a CLEAR operation, deleting all the triples in the default graph. Because empty graphs are not stored in the RDF model, the CLEAR operation always succeeds and is equivalent to a DROP operation. (For the same reason, the CREATE operation has no effect on the RDF model.)

-- Dataset before operation:
@prefix : <http://www.example.org/electronics/> 
#Default graph
:camera2 :name "Camera 2";
         :price 150;
         :cameraType :Camera .
#Empty graph :digitalCameras
#Graph :cameras
GRAPH :cameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera 
  :camera2 :name "Camera 2";
           :price 150;
           :cameraType :Camera .
}
#Graph :digCam
GRAPH :digCam {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}

-- Update operation:
BEGIN
  sem_apis.update_model('electronics',
   'CLEAR DEFAULT ');
END;
/

-- Dataset after operation:
@prefix : <http://www.example.org/electronics/> 
#Empty Default graph
#Empty graph :digitalCameras
#Graph :cameras
GRAPH :cameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
  :camera2 :name "Camera 2";
           :price 150;
           :cameraType :Camera .
}
#Graph :digCam
GRAPH :digCam {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}

Example 1-107 LOAD Operation

N-Triple and N-Quad files can be loaded from the local file system using the LOAD operation. An optional INTO clause can be used to load the file into a specific named graph. (The CREATE ANY DIRECTORY and DROP ANY DIRECTORY privileges are required for performing a LOAD operation.) This example loads the /home/oracle/example.nq N-Quad file into a semantic model.

Note that the use of an INTO clause with an N-Quad file will override any named graph information in the N-Quad file. In this example, INTO GRAPH :cameras overrides :myGraph for the first quad, so the subject, property, object triple component of this quad is inserted into the :cameras graph instead.

-- Datafile: /home/oracle/example.nq
<http://www.example.org/electronics/camera3> <http://www.example.org/electronics/name> "Camera 3" <http://www.example.org/electronics/myGraph> .
<http://www.example.org/electronics/camera3> <http://www.example.org/electronics/price> "125"^^<http://www.w3.org/2001/XMLSchema#decimal> .

-- Dataset before operation:
#Graph :cameras
GRAPH :cameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
  :camera2 :name "Camera 2";
           :price 150;
           :cameraType :Camera .
}
#Graph :digCam
GRAPH :digCam {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}

-- Update operation:
BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/>
    LOAD <file:///home/oracle/example.nq> INTO GRAPH :cameras');
END;
/

-- Dataset after operation:
@prefix : <http://www.example.org/electronics/> 
#Graph :cameras
GRAPH :cameras {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
  :camera2 :name "Camera 2";
           :price 150;
           :cameraType :Camera .
  :camera3 :name "Camera 3";
           :price 125.
}
#Graph :digCam
GRAPH :digCam {
  :camera1 :name "Camera 1";
           :cameraType :digitalCamera .
}

Several files under the same directory can be loaded in parallel with a single LOAD operation. To specify extra N-Triple or N-Quad files to be loaded, you can use the LOAD_OPTIONS hint. The files will be loaded in parallel using a degree of parallelism equal to the number of files. The following example shows how to load the files /home/oracle/example1.nq, /home/oracle/example2.nq, and /home/oracle/example3.nq into a semantic model. A degree of parallelism of 3 will automatically be used for this example.

BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/>
    LOAD <file:///home/oracle/example1.nq>',
   options=> ' LOAD_OPTIONS={ example2.nq example3.nq } ' );
END;
/

Related subtopics:

Parent topic: RDF Semantic Graph Overview

1.12.1 Tuning the Performance of SPARQL Update Operations

In some cases it may be necessary to tune the performance of SPARQL Update operations. Because SPARQL Update operations involve executing one or more SPARQL queries based on the WHERE clause in the UPDATE statement, the Best Practices for Query Performance also apply to SPARQL Update operations. The following considerations also apply:

Delete operations require an appropriate index on the application table (associated with the apply_model model in SEM_APIS.UPDATE_MODEL) for good performance. Assuming an application table named APP_TAB with the SDO_RDF_TRIPLE_S column named TRIPLE, an index similar to the following is recommended (this is the same index used by RDF Semantic Graph Support for Apache Jena):
```
-- Application table index for 
--  (graph_id, subject_id, predicate_id, canonical_object_id)
CREATE INDEX app_tab_idx ON app_tab app (
  BITAND(app.triple.rdf_m_id,79228162514264337589248983040)/4294967296,
  app.triple.rdf_s_id,
  app.triple.rdf_p_id,
  app.triple.rdf_c_id)
COMPRESS;
```
Performance-related SEM_MATCH options can be passed to the match_options parameter of SEM_APIS.UPDATE_MODEL, and performance-related options such as PARALLEL and DYNAMIC_SAMPLING can be specified in the options parameter of that procedure. The following example uses the options parameter to specify a degree of parallelism of 4 and an optimizer dynamic sampling level of 6 for the update. In addition, the example uses ALLOW_DUP=T as a match option when matching against the virtual model VM1.
```
BEGIN
  sem_apis.update_model(
   'electronics',
   'PREFIX : <http://www.example.org/electronics/> 
    INSERT { graph :digitalCameras { ?s ?p ?o } }
    WHERE  { ?s :cameraType :digitalCamera .
             ?s ?p ?o }',
   match_models=>sem_models('VM1'),
   match_options=>' ALLOW_DUP=T ',
   options=>' PARALLEL(4) DYNAMIC_SAMPLING(6) ');
END;
/
```

Inline Query Optimizer Hints can be specified in the WHERE clause. The following example extends the preceding example by using the HINT0 hint in the WHERE clause and the FINAL_VALUE_NL hint in the match_options parameter.

BEGIN
  sem_apis.update_model(
   'electronics',
   'PREFIX : <http://www.example.org/electronics/> 
    INSERT { graph :digitalCameras { ?s ?p ?o } }
    WHERE  { # HINT0={ LEADING(t0 t1) USE_NL(t0 t1)
             ?s :cameraType :digitalCamera .
             ?s ?p ?o }',
   match_models=>sem_models('VM1'),
   match_options=>' ALLOW_DUP=T FINAL_VALUE_NL ',
   options=>' PARALLEL(4) DYNAMIC_SAMPLING(6) ');
END;
/

Parent topic: Support for SPARQL Update Operations on a Semantic Model

1.12.2 Transaction Management with SPARQL Update Operations

You can exercise some control over the number of transactions used and whether they are automatically committed by a SEM_APIS.UPDATE_MODEL operation.

By default, the SEM_APIS.UPDATE_MODEL procedure executes in a single transaction that is either committed upon successful completion or rolled back if an error occurs. For example, the following call executes three update operations (separated by semicolons) in a single transaction:

BEGIN
  sem_apis.update_model('electronics',
   'PREFIX elec: <http://www.example.org/electronics/>
    PREFIX ecom: <http://www.example.org/ecommerce/> 
    # insert camera data
    INSERT DATA {
      elec:camera1 elec:name "Camera 1" .
      elec:camera1 elec:price 120 .
      elec:camera1 elec:cameraType elec:DigitalCamera .
      elec:camera2 elec:name "Camera 2" .
      elec:camera2 elec:price 150 .
      elec:camera2 elec:cameraType elec:DigitalCamera . }; 
    # insert ecom:price triples
    INSERT { ?c  ecom:price ?p }
    WHERE  { ?c  elec:price ?p };
    # delete elec:price triples
    DELETE WHERE { ?c elec:price ?p }');
END;
/

PL/SQL procedure successfully completed.

By contrast, the following example uses three separate SEM_APIS.UPDATE_MODEL calls to execute the same three update operations in three separate transactions:

BEGIN
  sem_apis.update_model('electronics',
   'PREFIX elec: <http://www.example.org/electronics/>
    PREFIX ecom: <http://www.example.org/ecommerce/> 
    # insert camera data
    INSERT DATA {
      elec:camera1 elec:name "Camera 1" .
      elec:camera1 elec:price 120 .
      elec:camera1 elec:cameraType elec:DigitalCamera .
      elec:camera2 elec:name "Camera 2" .
      elec:camera2 elec:price 150 .
      elec:camera2 elec:cameraType elec:DigitalCamera . }');
END;
/

PL/SQL procedure successfully completed.

BEGIN
  sem_apis.update_model('electronics',
   'PREFIX elec: <http://www.example.org/electronics/>
    PREFIX ecom: <http://www.example.org/ecommerce/> 
    # insert ecom:price triples
    INSERT { ?c  ecom:price ?p }
    WHERE  { ?c  elec:price ?p }');
END;
/

PL/SQL procedure successfully completed.

BEGIN
  sem_apis.update_model('electronics',
   'PREFIX elec: <http://www.example.org/electronics/>
    PREFIX ecom: <http://www.example.org/ecommerce/> 
    # insert elec:price triples
    DELETE WHERE { ?c elec:price ?p }');
END;
/

PL/SQL procedure successfully completed.

The AUTOCOMMIT=F option can be used to prevent separate transactions for each SEM_APIS.UPDATE_MODEL call. With this option, transaction management is the responsibility of the caller. The following example shows how to execute the update operations in the preceding example as a single transaction instead of three separate ones.

BEGIN
  sem_apis.update_model('electronics',
   'PREFIX elec: <http://www.example.org/electronics/>
    PREFIX ecom: <http://www.example.org/ecommerce/> 
    # insert camera data
    INSERT DATA {
      elec:camera1 elec:name "Camera 1" .
      elec:camera1 elec:price 120 .
      elec:camera1 elec:cameraType elec:DigitalCamera .
      elec:camera2 elec:name "Camera 2" .
      elec:camera2 elec:price 150 .
      elec:camera2 elec:cameraType elec:DigitalCamera . }',
   options=>' AUTOCOMMIT=F ');
END;
/

PL/SQL procedure successfully completed.

BEGIN
  sem_apis.update_model('electronics',
   'PREFIX elec: <http://www.example.org/electronics/>
    PREFIX ecom: <http://www.example.org/ecommerce/> 
    # insert ecom:price triples
    INSERT { ?c  ecom:price ?p }
    WHERE  { ?c  elec:price ?p }',
   options=>' AUTOCOMMIT=F ');
END;
/

PL/SQL procedure successfully completed.

BEGIN
  sem_apis.update_model('electronics',
   'PREFIX elec: <http://www.example.org/electronics/>
    PREFIX ecom: <http://www.example.org/ecommerce/> 
    # insert elec:price triples
    DELETE WHERE { ?c elec:price ?p }',
   options=>' AUTOCOMMIT=F ');
END;
/

PL/SQL procedure successfully completed.

COMMIT;

Commit complete.

However, the following cannot be used with the AUTOCOMMIT=F option:

Bulk operations (FORCE_BULK=T, DEL_AS_INS=T)
LOAD operations
Materialization of intermediate data (STREAMING=F)

Transaction Isolation Levels

Parent topic: Support for SPARQL Update Operations on a Semantic Model

1.12.2.1 Transaction Isolation Levels

Oracle Database supports three different transaction isolation levels: read committed, serializable, and read-only.

Read committed isolation level is the default. Queries in a transaction using this isolation level see only data that was committed before the query – not the transaction – began and any changes made by the transaction itself. This isolation level allows the highest degree of concurrency.

Serializable isolation level queries see only data that was committed before the transaction began and any changes made by the transaction itself.

Read-only isolation level behaves like serializable isolation level but data cannot be modified by the transaction.

SEM_APIS.UPDATE_MODEL supports read committed and serializable transaction isolation levels, and read committed is the default. SPARQL UPDATE operations are processed in the following basic steps.

A query is executed to obtain a set of triples to be deleted.
A query is executed to obtain a set of triples to be inserted.
Triples obtained in Step 1 are deleted.
Triples obtained in Step 2 are inserted.

With the default read committed isolation level, the underlying triple data may be modified by concurrent transactions, so each step may see different data. In addition, changes made by concurrent transactions will be visible to subsequent update operations within the same SEM_APIS.UPDATE_MODEL call. Note that steps 1 and 2 happen as a single step when using materialization of intermediate data (STREAMING=F), so underlying triple data cannot be modified between steps 1 and 2 with this option. See Support for Bulk Operations for more information about materialization of intermediate data.

Serializable isolation level can be used by specifying the SERIALIZABLE=T option. In this case, each step will only see data that was committed before the update model operation began, and multiple update operations executed in a single SEM_APIS.UPDATE_MODEL call will not see modifications made by concurrent update operations in other transactions. However, ORA-08177 errors will be raised if a SEM_APIS.UPDATE_MODEL execution tries to update triples that were modified by a concurrent transaction. When using SERIALIZABLE=T, the application should detect and handle ORA-08177 errors (for example, retry the update command if it could not be serialized on the first attempt).

The following cannot be used with the SERIALIZABLE=T option:

Bulk operations (FORCE_BULK=T, DEL_AS_INS=T)
LOAD operations
Materialization of intermediate data (STREAMING=F)

Parent topic: Transaction Management with SPARQL Update Operations

1.12.3 Support for Bulk Operations

SEM_APIS.UPDATE_MODEL supports bulk operations for efficient execution of large updates. The following options are provided; however, when using any of these bulk operations, serializable isolation (SERIALIZABLE=T) and autocommit false (AUTOCOMMMIT=F) cannot be used.

Parent topic: Support for SPARQL Update Operations on a Semantic Model

1.12.3.1 Materialization of Intermediate Data (STREAMING=F)

By default, SEM_APIS.UPDATE_MODEL executes two queries for a basic DELETE INSERT SPARQL Update operation: one query to find triples to delete and one query to find triples to insert. For some update operations with WHERE clauses that are expensive to evaluate, executing two queries may not give the best performance. In these cases, executing a single query for the WHERE clause, materializing the results, and then using the materialized results to construct triples to delete and triples to insert may give better performance. This approach incurs overhead from a DDL operation, but overall performance is likely to be better for complex update statements.

The following example update using this option (STREAMING=F). Note that STREAMING=F is not allowed with serializable isolation (SERIALIZABLE=T) or autocommit false (AUTOCOMMIT=F).

BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/> 
    DELETE { ?s ?p ?o } 
    INSERT { graph :digitalCameras { ?s ?p ?o } }
    WHERE  { ?s :cameraType :digitalCamera .
             ?s ?p ?o }',
  options=>' STREAMING=F ');
END;
/

Parent topic: Support for Bulk Operations

1.12.3.2 Using SEM_APIS.BULK_LOAD_FROM_STAGING_TABLE

For updates that insert a large number of triples (such as tens of thousands), the default approach of incremental DML on the application table may not give acceptable performance. In such cases, the FORCE_BULK=T option can be specified so that SEM_APIS.BULK_LOAD_FROM_STAGING_TABLE is used instead of incremental DML.

However, not all update operations can use this optimization. The FORCE_BULK=T option is only allowed for a SEM_APIS.UPDATE_MODEL call with either a single ADD operation or a single INSERT WHERE operation. The use of SEM_APIS.BULK_LOAD_FROM_STAGING_TABLE forces a series of commits and autonomous transactions, so the AUTOCOMMIT=F and SERIALIZABLE=T options are not allowed with FORCE_BULK=T. In addition, bulk load cannot be used with CLOB_UPDATE_SUPPORT=T.

SEM_APIS.BULK_LOAD_FROM_STAGING_TABLE allows various customizations through its flags parameter. SEM_APIS.UPDATE_MODEL supports the BULK_OPTIONS={ OPTIONS_STRING } flag so that OPTIONS_STRING can be passed into the flags input of SEM_APIS.BULK_LOAD_FROM_STAGING_TABLE to customize bulk load options. The following example shows a SEM_APIS.UPDATE_MODEL invocation using the FORCE_BULK=T option and BULK_OPTIONS flag.

BEGIN
  sem_apis.update_model('electronics',
   'PREFIX elec: <http://www.example.org/electronics/>
    PREFIX ecom: <http://www.example.org/ecommerce/> 
    INSERT { ?c  ecom:price ?p }
    WHERE  { ?c  elec:price ?p }',
   options=>' FORCE_BULK=T BULK_OPTIONS={  parallel=4 parallel_create_index } ');
END;
/

Parent topic: Support for Bulk Operations

1.12.3.3 Using Delete as Insert (DEL_AS_INS=T)

For updates that delete a large number of triples (such as tens of thousands), the default approach of incremental DML on the application table may not give acceptable performance. For such cases, the DEL_AS_INS=T option can be specified. With this option, a large delete operation is implemented as INSERT, TRUNCATE, and EXCHANGE PARTITION operations.

The use of DEL_AS_INS=T causes a series of commits and autonomous transactions, so this option cannot be used with SERIALIZABLE=T or AUTOCOMMIT=F. In addition, this option can only be used with SEM_APIS.UPDATE_MODEL calls that involve a single DELETE WHERE operation, a single DROP operation, or a single CLEAR operation.

Delete as insert internally uses SEM_APIS.MERGE_MODELS during intermediate operations. The string OPTIONS_STRING from the MM_OPTIONS={ OPTIONS_STRING } flag can be specified to customize options for merging. The following example shows a SEM_APIS.UPDATE_MODEL invocation using the DEL_AS_INS=T option and MM_OPTIONS flag.

BEGIN
  sem_apis.update_model('electronics',
   'CLEAR NAMED',
   options=>' DEL_AS_INS=T MM_OPTIONS={  dop=4 } ');
END;
/

Parent topic: Support for Bulk Operations

1.12.4 Setting UPDATE_MODEL Options at the Session Level

Some settings that affect the SEM_APIS.UPDATE_MODEL procedure’s behavior can be modified at the session level through the use of the special MDSYS.SDO_SEM_UPDATE_CTX.SET_PARAM procedure. The following options can be set to true or false at the session level: autocommit, streaming, strict_bnode, and clob_support.

The MDSYS.SDO_SEM_UPDATE_CTX contains the following subprograms to get and set SEM_APIS.UPDATE_MODEL parameters at the session level:

SQL> describe mdsys.sdo_sem_update_ctx
FUNCTION GET_PARAM RETURNS VARCHAR2
 Argument Name                  Type                    In/Out Default?
 ------------------------------ ----------------------- ------ --------
 NAME                           VARCHAR2                IN
PROCEDURE SET_PARAM
 Argument Name                  Type                    In/Out Default?
 ------------------------------ ----------------------- ------ --------
 NAME                           VARCHAR2                IN
 VALUE                          VARCHAR2                IN

The following example causes all subsequent calls to the SEM_APIS.UPDATE_MODEL procedure to use the AUTOCOMMIT=F setting, until the end of the session or the next call to SEM_APIS.UPDATE_MODEL that specifies a different autocommit value.

begin
  mdsys.sdo_sem_update_ctx.set_param('autocommit','false');
end;
/

Parent topic: Support for SPARQL Update Operations on a Semantic Model

1.12.5 Load Operations: Special Considerations for SPARQL Update

If a load operation is performed using the SEM_APIS.UPDATE_MODEL procedure and if the loaded document contains values longer than 4000 bytes, triples containing such values are not inserted into the model. When loading finishes, a file with “.bad” extension is created in the same directory where the loaded document is located. The .bad file contains triples with long values. They can be inserted using INSERT statements as explained in Loading Semantic Data Using INSERT Statements, or they can be inserted with INSERT DATA statements executed through the SEM_APIS.UPDATE_MODEL procedure with the CLOB_UPDATE_SUPPORT=T option.

To perform the load operation, triples from the document are loaded into a staging table that is created under the current schema. If a load operation is interrupted, but the staging table has already been created, loading can be resumed with the RESUME_LOAD=T keyword in the options parameter.

Parent topic: Support for SPARQL Update Operations on a Semantic Model

1.12.6 Long Literals: Special Considerations for SPARQL Update

By default, SPARQL Update operations do not manipulate values longer than 4000 bytes. To enable long literals support, specify CLOB_UPDATE_SUPPORT=T in the options parameter with the SEM_APIS.UPDATE_MODEL procedure.

Bulk load does not work for long literals; the FORCE_BULK=T option is ignored when used with the CLOB_UPDATE_SUPPORT=T option.

Parent topic: Support for SPARQL Update Operations on a Semantic Model

1.12.7 Blank Nodes: Special Considerations for SPARQL Update

Some update operations only affect the graph of a set of RDF triples. Specifically, these operations are ADD, COPY and MOVE. For example, the MOVE operation example in Support for SPARQL Update Operations on a Semantic Model can be performed only updating triples having :digitalCameras as the graph. However, the performance of such operations can be improved by using ID-only operations over the RDF model. To run a large ADD, COPY, or MOVE operation as an ID-only operation, you can specify the STRICT_BNODE=F hint in the options parameter for the SEM_APIS.UPDATE_MODEL procedure.

ID-only operations may lead to incorrect blank nodes, however, because no two graphs should share the same blank node. RDF Semantic Graph uses a blank node prefixing scheme based on the model and graph combination that contains a blank node. These prefixes ensure that blank node identifiers are unique across models and graphs. An ID-only approach for ADD, COPY, and UPDATE operations does not update blank node prefixes.

Example 1-108 ID-Only Update Causing Incorrect Blank Node Values

The update in the following example leads to the same blank node subject for both triples in graphs :cameras and :cameras2. This can be verified running the provided SEM_MATCH query.

BEGIN
  sem_apis.update_model('electronics',
   'PREFIX : <http://www.example.org/electronics/> 
    INSERT DATA { 
       GRAPH :cameras { :camera2 :owner _:bn1 .
                        _:bn1 :name "Axel" }
    };
    COPY :cameras TO :cameras2',
   options=>' STRICT_BNODE=F ');
END;
/

SELECT count(s)
FROM TABLE( SEM_MATCH('
  PREFIX : <http://www.example.org/electronics/> 
  SELECT * 
  WHERE { { graph :cameras  {?s :name "Axel"  } }
          { graph :cameras2 {?s :name "Axel"  } } }
', sem_models('electronics'),null,null,null,null,' STRICT_DEFAULT=T '));

To avoid such errors, you should specify the STRICT_BNODE=F hint in the options parameter for the SEM_APIS.UPDATE_MODEL procedure only when you are sure that blank nodes are not involved in the ADD, COPY, or MOVE update operation.

However, ADD, COPY, and MOVE operations on large graphs with the STRICT_BNODE=F option may run significantly faster than they would run using the default method. If you need to run a series of ID-only updates, another option is to use the STRICT_BNODE=F option, and then execute the SEM_APIS.CLEANUP_BNODES procedure at the end. This approach resets the prefix of all blank nodes in a given model, which effectively corrects ("cleans up") all erroneous blank node labels.

Note that this two-step strategy should not be used with a small number of ADD, COPY, or MOVE operations. Performing a few operations using the default approach will execute faster than running a few ID-only operations and then executing the SEM_APIS.CLEANUP_BNODES procedure.

The following example corrects blank nodes in a semantic model named electronics.

EXECUTE sem_apis.cleanup_bnodes('electronics');

Parent topic: Support for SPARQL Update Operations on a Semantic Model

1.13 Enhanced RDF ORDER BY Query Processing

Effective with Oracle Database Release 12.2, queries on RDF data that use SPARQL ORDER BY semantics are processed more efficiently than in previous releases.

This internal efficiency involves the use of the ORDER_TYPE, ORDER_NUM, and ORDER_DATE columns in the MDSYS.RDF_VALUE$ metadata table (documented in Statements). The values for these three columns are populated during loading, and this enables ORDER BY queries to reduce internal function calls and to execute faster.

Effective with Oracle Database Release 12.2, the procedure SEM_APIS.ADD_DATATYPE_INDEX creates an index on the ORDER_NUM column for numeric types (xsd:float, xsd:double, and xsd:decimal and all of its subtypes) and an index on ORDER_DATE column for date-related types (xsd:date, xsd:time, and xsd:dateTime) instead of a function-based index as in previous versions. If you want to continue using a function-based index for these data types, you should use the FUNCTION=T option of the SEM_APIS.ADD_DATATYPE_INDEX procedure.. For example:

EXECUTE sem_apis.add_datatype_index('http://www.w3.org/2001/XMLSchema#decimal', options=>'FUNCTION=T');
EXECUTE sem_apis.add_datatype_index('http://www.w3.org/2001/XMLSchema#date',options=>'FUNCTION=T');

Parent topic: RDF Semantic Graph Overview

1.14 Quick Start for Using Semantic Data

To work with semantic data in an Oracle database, follow these general steps.

Create a tablespace for the system tables. You must be connected as a user with appropriate privileges to create the tablespace. The following example creates a tablespace named RDF_TBLSPACE:
```
CREATE TABLESPACE rdf_tblspace
 DATAFILE '/oradata/orcl/rdf_tblspace.dat' SIZE 1024M REUSE
 AUTOEXTEND ON NEXT 256M MAXSIZE UNLIMITED
 SEGMENT SPACE MANAGEMENT AUTO;
```
Create a semantic data network.
Creating a semantic data network adds semantic data support to an Oracle database. You must create a semantic data network as a user with DBA privileges, specifying a valid tablespace with adequate space. Create the network only once for an Oracle database.

The following example creates a semantic data network using a tablespace named RDF_TBLSPACE (which must already exist):
```
EXECUTE SEM_APIS.CREATE_SEM_NETWORK('rdf_tblspace');
```
Connect as the database user under whose schema you will store your semantic data; do not perform the following steps while connected as SYS, SYSTEM, or MDSYS.
Create a table to store references to the semantic data. (You do not need to be connected as a user with DBA privileges for this step and the remaining steps.)
This table must contain a column of type SDO_RDF_TRIPLE_S, which will contain references to all data associated with a single model.

The following example creates a table named ARTICLES_RDF_DATA with one column to hold the data for triples:
```
CREATE TABLE articles_rdf_data (triple SDO_RDF_TRIPLE_S);
```
Create a model.
When you create a model, you specify the model name, the table to hold references to semantic data for the model, and the column of type SDO_RDF_TRIPLE_S in that table.

The following command creates a model named ARTICLES, which will use the table created in the preceding step.
```
EXECUTE SEM_APIS.CREATE_SEM_MODEL('articles', 'articles_rdf_data', 'triple');
```

After you create the model, you can insert triples into the table, as shown in the examples in Semantic Data Examples (PL/SQL and Java).

Parent topic: RDF Semantic Graph Overview

1.15 Semantic Data Examples (PL/SQL and Java)

PL/SQL examples are provided in this guide.

In addition to the examples in this guide, see the sample code at http://www.oracle.com/technetwork/indexes/samplecode/semantic-sample-522114.html.

Parent topic: RDF Semantic Graph Overview

1.15.1 Example: Journal Article Information

This section presents a simplified PL/SQL example of model for statements about journal articles. Example 1-109 contains descriptive comments, refers to concepts that are explained in this chapter, and uses functions and procedures documented in SEM_APIS Package Subprograms.

Example 1-109 Using a Model for Journal Article Information

-- Basic steps:
-- After you have connected as a privileged user and called 
-- SEM_APIS.CREATE_SEM_NETWORK to add RDF support,
-- connect as a regular database user and do the following.
-- 1. For each desired model, create a table to hold its data.
-- 2. For each model, create a model (SEM_APIS.CREATE_SEM_MODEL).
-- 3. For each table to hold semantic data, insert data into the table.
-- 4. Use various subprograms and constructors.
 
-- Create the table to hold data for the model. Only one column: data for triples.
CREATE TABLE articles_rdf_data (triple SDO_RDF_TRIPLE_S);
 
-- Create the model.
EXECUTE SEM_APIS.CREATE_SEM_MODEL('articles', 'articles_rdf_data', 'triple');
 
-- Information to be stored about some fictitious articles:
-- Article1, titled "All about XYZ" and written by Jane Smith, refers 
--   to Article2 and Article3.
-- Article2, titled "A review of ABC" and written by Joe Bloggs, 
--   refers to Article3.
-- Seven SQL statements to store the information. In each statement:
-- Each article is referred to by its complete URI The URIs in
--   this example are fictitious.
-- Each property is referred to by the URL for its definition, as 
--   created by the Dublin Core Metadata Initiative.
 
-- Insert rows into the table.
 
-- Article1 has the title "All about XYZ".
INSERT INTO articles_rdf_data VALUES (
  SDO_RDF_TRIPLE_S ('articles','<http://nature.example.com/Article1>',
    '<http://purl.org/dc/elements/1.1/title>','"All about XYZ"'));
 
-- Article1 was created (written) by Jane Smith.
INSERT INTO articles_rdf_data VALUES (
  SDO_RDF_TRIPLE_S ('articles','<http://nature.example.com/Article1>',
    '<http://purl.org/dc/elements/1.1/creator>',
    '"Jane Smith"'));
 
-- Article1 references (refers to) Article2.
INSERT INTO articles_rdf_data VALUES (
  SDO_RDF_TRIPLE_S ('articles',
    '<http://nature.example.com/Article1>',
    '<http://purl.org/dc/terms/references>',
    '<http://nature.example.com/Article2>'));
 
-- Article1 references (refers to) Article3.
INSERT INTO articles_rdf_data VALUES (
  SDO_RDF_TRIPLE_S ('articles',
    '<http://nature.example.com/Article1>',
    '<http://purl.org/dc/terms/references>',
    '<http://nature.example.com/Article3>'));
 
-- Article2 has the title "A review of ABC".
INSERT INTO articles_rdf_data VALUES (
  SDO_RDF_TRIPLE_S ('articles',
    '<http://nature.example.com/Article2>',
    '<http://purl.org/dc/elements/1.1/title>',
    '"A review of ABC"'));
 
-- Article2 was created (written) by Joe Bloggs.
INSERT INTO articles_rdf_data VALUES (
  SDO_RDF_TRIPLE_S ('articles',
    '<http://nature.example.com/Article2>',
    '<http://purl.org/dc/elements/1.1/creator>',
    '"Joe Bloggs"'));
 
-- Article2 references (refers to) Article3.
INSERT INTO articles_rdf_data VALUES (
  SDO_RDF_TRIPLE_S ('articles',
    '<http://nature.example.com/Article2>',
    '<http://purl.org/dc/terms/references>',
    '<http://nature.example.com/Article3>'));
 
COMMIT;
 
-- Query semantic data.
 
SELECT SEM_APIS.GET_MODEL_ID('articles') AS model_id FROM DUAL;
 
SELECT SEM_APIS.GET_TRIPLE_ID(
  'articles',
  '<http://nature.example.com/Article2>',
  '<http://purl.org/dc/terms/references>',
  '<http://nature.example.com/Article3>') AS RDF_triple_id FROM DUAL;
 
SELECT SEM_APIS.IS_TRIPLE(
  'articles',
  '<http://nature.example.com/Article2>',
  '<http://purl.org/dc/terms/references>',
  '<http://nature.example.com/Article3>') AS is_triple FROM DUAL;
 
-- Use SDO_RDF_TRIPLE_S member functions in queries.
 
-- Find all articles that reference Article2.
SELECT a.triple.GET_SUBJECT() AS subject
  FROM articles_rdf_data a
  WHERE a.triple.GET_PROPERTY() = '<http://purl.org/dc/terms/references>' AND
    TO_CHAR(a.triple.GET_OBJECT()) = '<http://nature.example.com/Article2>';

-- Find all triples with Article1 as subject.
SELECT a.triple.GET_TRIPLE() AS triple
  FROM articles_rdf_data a
  WHERE a.triple.GET_SUBJECT() = '<http://nature.example.com/Article1>';

-- Find all objects where the subject is Article1.
SELECT a.triple.GET_OBJECT() AS object 
  FROM articles_rdf_data a 
WHERE a.triple.GET_SUBJECT() = '<http://nature.example.com/Article1>';

-- Find all triples where Jane Smith is the object.
SELECT a.triple.GET_TRIPLE() AS triple
  FROM articles_rdf_data a 
  WHERE TO_CHAR(a.triple.GET_OBJECT()) = '"Jane Smith"';

Parent topic: Semantic Data Examples (PL/SQL and Java)

1.15.2 Example: Family Information

This section presents a simplified PL/SQL example of a model for statements about family tree (genealogy) information. Example 1-109 contains descriptive comments, refers to concepts that are explained in this chapter, and uses functions and procedures documented in SEM_APIS Package Subprograms.

The family relationships in this example reflect the family tree shown in Figure 1-3. This figure also shows some of the information directly stated in the example: Cathy is the sister of Jack, Jack and Tom are male, and Cindy is female.

Figure 1-3 Family Tree for RDF Example

Description of "Figure 1-3 Family Tree for RDF Example"

Example 1-110 Using a Model for Family Information

-- Preparation: create tablespace; enable RDF support.
-- Connect as a privileged user. Example: CONNECT SYSTEM/password-for-SYSTEM
-- Create a tablespace for the RDF data. Example:
CREATE TABLESPACE rdf_tblspace 
  DATAFILE 'rdf_tblspace.dat' 
    SIZE 128M REUSE
    AUTOEXTEND ON NEXT 128M MAXSIZE 4G
  SEGMENT SPACE MANAGEMENT AUTO;

-- Call SEM_APIS.CREATE_SEM_NETWORK to enable RDF support. Example:
EXECUTE SEM_APIS.CREATE_SEM_NETWORK('rdf_tblspace');

-- Connect as the user that is to perform the RDF operations (not SYSTEM), 
-- and do the following:
-- 1. For each desired model, create a table to hold its data.
-- 2. For each model, create a model (SEM_APIS.CREATE_SEM_MODEL).
-- 3. For each table to hold semantic data, insert data into the table.
-- 4. Use various subprograms and constructors.

-- Create the table to hold data for the model.
CREATE TABLE family_rdf_data (triple SDO_RDF_TRIPLE_S);
 
-- Create the model.
execute SEM_APIS.create_sem_model('family', 'family_rdf_data', 'triple');
 
-- Insert rows into the table. These express the following information:
-----------------
-- John and Janice have two children, Suzie and Matt.
-- Matt married Martha, and they have two children:
--   Tom (male, height 5.75) and Cindy (female, height 06.00).
-- Suzie married Sammy, and they have two children:
--   Cathy (height 5.8) and Jack (male, height 6).
 
-- Person is a class that has two subslasses: Male and Female.
-- parentOf is a property that has two subproperties: fatherOf and motherOf.
-- siblingOf is a property that has two subproperties: brotherOf and sisterOf.
-- The domain of the fatherOf and brotherOf properties is Male.
-- The domain of the motherOf and sisterOf properties is Female.
------------------------
 
-- John is the father of Suzie.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/John>', 
'<http://www.example.org/family/fatherOf>', 
'<http://www.example.org/family/Suzie>'));
 
-- John is the father of Matt.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/John>', 
'<http://www.example.org/family/fatherOf>', 
'<http://www.example.org/family/Matt>'));
 
-- Janice is the mother of Suzie.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Janice>', 
'<http://www.example.org/family/motherOf>', 
'<http://www.example.org/family/Suzie>'));
 
-- Janice is the mother of Matt.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Janice>', 
'<http://www.example.org/family/motherOf>', 
'<http://www.example.org/family/Matt>'));
 
-- Sammy is the father of Cathy.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Sammy>', 
'<http://www.example.org/family/fatherOf>', 
'<http://www.example.org/family/Cathy>'));
 
-- Sammy is the father of Jack.
INSERT INTO family_rdf_data VALUES (
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Sammy>', 
'<http://www.example.org/family/fatherOf>', 
'<http://www.example.org/family/Jack>'));
 
-- Suzie is the mother of Cathy.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Suzie>', 
'<http://www.example.org/family/motherOf>', 
'<http://www.example.org/family/Cathy>'));
 
-- Suzie is the mother of Jack.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Suzie>', 
'<http://www.example.org/family/motherOf>', 
'<http://www.example.org/family/Jack>'));
 
-- Matt is the father of Tom.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Matt>', 
'<http://www.example.org/family/fatherOf>', 
'<http://www.example.org/family/Tom>'));
 
-- Matt is the father of Cindy
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Matt>', 
'<http://www.example.org/family/fatherOf>', 
'<http://www.example.org/family/Cindy>'));
 
-- Martha is the mother of Tom.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Martha>', 
'<http://www.example.org/family/motherOf>', 
'<http://www.example.org/family/Tom>'));
 
-- Martha is the mother of Cindy. 
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Martha>', 
'<http://www.example.org/family/motherOf>', 
'<http://www.example.org/family/Cindy>'));
 
-- Cathy is the sister of Jack.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Cathy>', 
'<http://www.example.org/family/sisterOf>', 
'<http://www.example.org/family/Jack>'));
 
-- Jack is male.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Jack>', 
'<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>',
'<http://www.example.org/family/Male>'));
 
-- Tom is male.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Tom>', 
'<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>',
'<http://www.example.org/family/Male>'));
 
-- Cindy is female.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Cindy>', 
'<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>',
'<http://www.example.org/family/Female>'));
 
-- Person is a class.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Person>', 
'<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>',
'<http://www.w3.org/2000/01/rdf-schema#Class>'));
 
-- Male is a subclass of Person.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Male>', 
'<http://www.w3.org/2000/01/rdf-schema#subClassOf>',
'<http://www.example.org/family/Person>'));
 
-- Female is a subclass of Person. 
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Female>', 
'<http://www.w3.org/2000/01/rdf-schema#subClassOf>',
'<http://www.example.org/family/Person>'));
 
-- siblingOf is a property.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/siblingOf>', 
'<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>',
'<http://www.w3.org/1999/02/22-rdf-syntax-ns#Property>'));
 
-- parentOf is a property.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/parentOf>', 
'<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>',
'<http://www.w3.org/1999/02/22-rdf-syntax-ns#Property>'));
 
-- brotherOf is a subproperty of siblingOf.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/brotherOf>', 
'<http://www.w3.org/2000/01/rdf-schema#subPropertyOf>',
'<http://www.example.org/family/siblingOf>'));
 
-- sisterOf is a subproperty of siblingOf.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/sisterOf>', 
'<http://www.w3.org/2000/01/rdf-schema#subPropertyOf>',
'<http://www.example.org/family/siblingOf>'));
 
-- A brother is male.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/brotherOf>', 
'<http://www.w3.org/2000/01/rdf-schema#domain>',
'<http://www.example.org/family/Male>'));
 
-- A sister is female.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/sisterOf>', 
'<http://www.w3.org/2000/01/rdf-schema#domain>',
'<http://www.example.org/family/Female>'));
 
-- fatherOf is a subproperty of parentOf.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/fatherOf>', 
'<http://www.w3.org/2000/01/rdf-schema#subPropertyOf>',
'<http://www.example.org/family/parentOf>'));
 
-- motherOf is a subproperty of parentOf.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/motherOf>', 
'<http://www.w3.org/2000/01/rdf-schema#subPropertyOf>',
'<http://www.example.org/family/parentOf>'));
 
-- A father is male.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/fatherOf>', 
'<http://www.w3.org/2000/01/rdf-schema#domain>',
'<http://www.example.org/family/Male>'));
 
-- A mother is female.
INSERT INTO family_rdf_data VALUES ( 
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/motherOf>', 
'<http://www.w3.org/2000/01/rdf-schema#domain>',
'<http://www.example.org/family/Female>'));
 
-- Use SET ESCAPE OFF to prevent the caret (^) from being
-- interpreted as an escape character. Two carets (^^) are
-- used to represent typed literals.
SET ESCAPE OFF;
 
-- Cathy's height is 5.8 (decimal).
INSERT INTO family_rdf_data VALUES (
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Cathy>', 
'<http://www.example.org/family/height>',
'"5.8"^^xsd:decimal'));
 
-- Jack's height is 6 (integer).
INSERT INTO family_rdf_data VALUES (
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Jack>', 
'<http://www.example.org/family/height>',
'"6"^^xsd:integer'));
 
-- Tom's height is 05.75 (decimal).
INSERT INTO family_rdf_data VALUES (
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Tom>', 
'<http://www.example.org/family/height>',
'"05.75"^^xsd:decimal'));
 
-- Cindy's height is 06.00 (decimal).
INSERT INTO family_rdf_data VALUES (
SDO_RDF_TRIPLE_S('family', 
'<http://www.example.org/family/Cindy>', 
'<http://www.example.org/family/height>',
'"06.00"^^xsd:decimal'));
 
COMMIT;
 
-- RDFS inferencing in the family model
BEGIN
  SEM_APIS.CREATE_ENTAILMENT(
    'rdfs_rix_family',
    SEM_Models('family'),
    SEM_Rulebases('RDFS'));
END;
/
 
-- Select all males from the family model, without inferencing.
-- (Returns only Jack and Tom.)
SELECT m
  FROM TABLE(SEM_MATCH(
    '{?m rdf:type :Male}',
    SEM_Models('family'),
    null,
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));
 
-- Select all males from the family model, with RDFS inferencing.
-- (Returns Jack, Tom, John, Sammy, and Matt.)
SELECT m
  FROM TABLE(SEM_MATCH(
    '{?m rdf:type :Male}',
    SEM_Models('family'),
    SDO_RDF_Rulebases('RDFS'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));
 
-- General inferencing in the family model
 
EXECUTE SEM_APIS.CREATE_RULEBASE('family_rb');
 
INSERT INTO mdsys.semr_family_rb VALUES(
  'grandparent_rule',
  '(?x :parentOf ?y) (?y :parentOf ?z)',
  NULL,
  '(?x :grandParentOf ?z)', 
  SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')));
 
COMMIT;
 
-- Because a new rulebase has been created, and it will be used in the
-- entailment, drop the preceding entailment and then re-create it.
EXECUTE SEM_APIS.DROP_ENTAILMENT ('rdfs_rix_family');
 
-- Re-create the entailment.
BEGIN
  SEM_APIS.CREATE_ENTAILMENT(
    'rdfs_rix_family',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'));
END;
/
 
-- Select all grandfathers and their grandchildren from the family model, 
-- without inferencing. (With no inferencing, no results are returned.)
SELECT x grandfather, y grandchild
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male}',
    SEM_Models('family'),
    null, 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));
 
-- Select all grandfathers and their grandchildren from the family model.
-- Use inferencing from both the RDFS and family_rb rulebases.
SELECT x grandfather, y grandchild
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male}',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')),
    null));
 
-- Set up to find grandfathers of tall (>= 6) grandchildren
-- from the family model, with RDFS inferencing and
-- inferencing using the "family_rb" rulebase.
 
UPDATE mdsys.semr_family_rb SET
  antecedents = '(?x :parentOf ?y) (?y :parentOf ?z) (?z :height ?h)',
  filter = '(h >= ''6'')',
  aliases = SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/'))
WHERE rule_name = 'GRANDPARENT_RULE';
 
-- Because the rulebase has been updated, drop the preceding entailment, 
-- and then re-create it.
EXECUTE SEM_APIS.DROP_ENTAILMENT ('rdfs_rix_family');
 
-- Re-create the entailment.
BEGIN
  SEM_APIS.CREATE_ENTAILMENT(
    'rdfs_rix_family',
    SEM_Models('family'),
    SEM_Rulebases('RDFS','family_rb'));
END;
/
 
-- Find the entailment that was just created (that is, the
-- one based on the specified model and rulebases).
SELECT SEM_APIS.LOOKUP_ENTAILMENT(SEM_MODELS('family'),
  SEM_RULEBASES('RDFS','family_rb')) AS lookup_entailment FROM DUAL;
 
-- Select grandfathers of tall (>= 6) grandchildren, and their
-- tall grandchildren.
SELECT x grandfather, y grandchild
  FROM TABLE(SEM_MATCH(
    '{?x :grandParentOf ?y . ?x rdf:type :Male}',
    SEM_Models('family'),
    SEM_RuleBases('RDFS','family_rb'), 
    SEM_ALIASES(SEM_ALIAS('','http://www.example.org/family/')), 
    null));

Parent topic: Semantic Data Examples (PL/SQL and Java)

1.16 Software Naming Changes Since Release 11.1

Because the support for semantic data has been expanded beyond the original focus on RDF, the names of many software objects (PL/SQL packages, functions and procedures, system tables and views, and so on) have been changed as of Oracle Database Release 11.1.

In most cases, the change is to replace the string RDF with SEM. although in some cases it may be to replace SDO_RDF with SEM.

All valid code that used the pre-Release 11.1 names will continue to work; your existing applications will not be broken. However, it is suggested that you change old applications to use new object names, and you should use the new names for any new applications. This manual will document only the new names.

Table 1-20 lists the old and new names for some objects related to support for semantic technologies, in alphabetical order by old name.

Table 1-20 Semantic Technology Software Objects: Old and New Names

Old Name	New Name
RDF_ALIAS data type	SEM_ALIAS
RDF_MODEL$ view	SEM_MODEL$
RDF_RULEBASE_INFO view	SEM_RULEBASE_INFO
RDF_RULES_INDEX_DATASETS view	SEM_RULES_INDEX_DATASETS
RDF_RULES_INDEX_INFO view	SEM_RULES_INDEX_INFO
RDFI_rules-index-name view	SEMI_rules-index-name
RDFM_model-name view	SEMM_model-name
RDFR_rulebase-name view	SEMR_rulebase-name
SDO_RDF package	SEM_APIS
SDO_RDF_INFERENCE package	SEM_APIS
SDO_RDF_MATCH table function	SEM_MATCH
SDO_RDF_MODELS data type	SEM_MODELS
SDO_RDF_RULEBASES data type	SEM_RULEBASES

Parent topic: RDF Semantic Graph Overview

1.17 For More Information About RDF Semantic Graph

More information is available about RDF Semantic Graph support and related topics.

See the following resources:

Oracle Spatial and Graph RDF Semantic Graph page (OTN), which includes links for downloads, technical and business white papers, a discussion forum, and other sources of information: http://www.oracle.com/technetwork/database/options/spatialandgraph/overview/rdfsemantic-graph-1902016.html
World Wide Web Consortium (W3C) RDF Primer: http://www.w3.org/TR/rdf-primer/
World Wide Web Consortium (W3C) OWL Web Ontology Language Reference: http://www.w3.org/TR/owl-ref/

Parent topic: RDF Semantic Graph Overview

1.18 Required Migration of Pre-12.2 Semantic Data

If you have any semantic data created using Oracle Database 11.1. 11.2, or 12.1, then before you use it in an Oracle Database 12.2 environment, you must migrate this data.

To perform the migration, use the SEM_APIS.MIGRATE_DATA_TO_CURRENT procedure. This applies not only to your existing semantic data, but also to any other semantic data introduced into your environment if that data was created using Oracle Database 11.1. 11.2, or 12.1

The reason for this requirement is for optimal performance of queries that use ORDER BY. Effective with Release 12.2, Oracle Database creates, populates, and uses the ORDER_TYPE, ORDER_NUM, and ORDER_DATE columns (new in Release 12.2) in the RDF_VALUE$ table (described in Statements). The SEM_APIS.MIGRATE_DATA_TO_CURRENT procedure populates these order-related columns. If you do not do this, those columns will be null for existing data.

You run this procedure after upgrading to Oracle Database Release 12.2. If you later bring into your Release 12.2 environment any semantic data that was created using an earlier release, you must also run the procedure before using that data. Running the procedure can take a long time with large amounts of semantic data, so consider that in deciding when to tun it. (Note that using the INS_AS_SEL=T option improves the performance of the SEM_APIS.MIGRATE_DATA_TO_CURRENT procedure with large data sets.)

Parent topic: RDF Semantic Graph Overview