SEM_APIS.CREATE_INFERRED_GRAPH

Format

SEM_APIS.CREATE_INFERRED_GRAPH(
     inferred_graph_name        IN VARCHAR2,
     rdf_graphs_in              IN SEM_MODELS,
     rulebases_in               IN SEM_RULEBASES,
     passes                     IN NUMBER DEFAULT SEM_APIS.REACH_CLOSURE,
     inf_components_in          IN VARCHAR2 DEFAULT NULL,
     options                    IN VARCHAR2 DEFAULT NULL,
     delta_in                   IN SEM_MODELS DEFAULT NULL,
     label_gen                  IN RDFSA_LABELGEN DEFAULT NULL,
     include_named_g            IN SEM_GRAPHS DEFAULT NULL,
     include_default_g          IN SEM_MODELS DEFAULT NULL,
     include_all_g              IN SEM_MODELS DEFAULT NULL,
     inf_ng_name                IN VARCHAR2 DEFAULT NULL,
     inf_ext_user_func_name     IN VARCHAR2 DEFAULT NULL,
     ols_ladder_inf_lbl_sec     IN VARCHAR2 DEFAULT NULL,
     network_owner              IN VARCHAR2 DEFAULT NULL,
     network_name               IN VARCHAR2 DEFAULT NULL);

Description

Creates an inferred graph (rules index) that can be used to perform OWL or RDFS inferencing, and optionally use user-defined rules.

Parameters

inferred_graph_name

Name of the inferred graph to be created.

rdf_graphs_in

One or more RDF graph names. Its data type is SEM_MODELS, which has the following definition: TABLE OF VARCHAR2(25)

rulebases_in

One or more rulebase names. Its data type is SEM_RULEBASES, which has the following definition: TABLE OF VARCHAR2(25). Rules and rulebases are explained in Inferencing: Rules and Rulebases.

passes

The number of rounds that the inference engine should run. The default value is SEM_APIS.REACH_CLOSURE, which means the inference engine will run till a closure is reached. If the number of rounds specified is less than the number of actual rounds needed to reach a closure, the status of the inferred graph will then be set to INCOMPLETE.

inf_components_in

A comma-delimited string of keywords representing inference components, for performing selective or component-based inferencing. If this parameter is null, the default set of inference components is used. See the Usage Notes for more information about inference components.

options

A comma-delimited string of options to control the inference process by overriding the default inference behavior. To enable an option, specify option-name=T; to disable an option, you can specify option-name=F. The available option-name values are COL_COMPRESS, DEST_MODEL, DISTANCE,DOP, ENTAIL_ANYWAY, HASH_PART, INC, LOCAL_NG_INF, OPT_SAMEAS, RAW8, PROOF, USER_RULES, and MATERIALIZE_TRANS. See the Usage Notes for explanations of each value.

delta_in

If incremental inference is in effect, specifies one or more RDF graphs on which to perform incremental inference. Its data type is SEM_MODELS, which has the following definition: TABLE OF VARCHAR2(25)

The triples in the first RDF graph in delta_in are copied to the first RDF graph in rdf_graphs_in, and the inferred graph (rules index) in rules_index_in is updated; then the triples in the second RDF graph (if any) in delta_in are copied to the second RDF graph (if any) in rdf_graphs_in, and the inferred graph in rules_index_in is updated; and so on until all triples are copied and the inferred graph is updated. (The delta_in parameter has no effect if incremental inference is not enabled for the inferred graph.)

label_gen

An instance of RDFSA_LABELGEN or a subtype of it, defining the logic for generating Oracle Label Security (OLS) labels for inferred triples. What you specify for this parameter depends on whether you use the default label generator or a custom label generator:

include_named_g

Causes all triples from the specified named graphs (across all source RDF graphs) to participate in named graph based global inference (NGGI, explained in Named Graph Based Global Inference (NGGI)). For example, include_named_g => sem_graphs('<urn:G1>','<urn:G2>') implies that triples from named graphs G1 and G2 will be included in NGGI.

Its data type is RDF_GRAPHS, which has the following definition: TABLE OF VARCHAR2(4000).

include_default_g

Causes all triples with a null graph name in the specified SEM_MODELS to participate in named graph based global inference (NGGI, explained in Named Graph Based Global Inference (NGGI)). For example, include_default_g => sem_models('m1') causes all triples with a null graph name from M1 to be included in NGGI.

include_all_g

Causes all triples, regardless of their graph name values, in the specified models to participate in named graph based global inference (NGGI, explained in Named Graph Based Global Inference (NGGI)). For example, include_all_g => sem_models('m2') causes all triples in M2 to be included in NGGI.

inf_ng_name

Assigns the specified graph name to all the new triples inferred by the named graph based global inference (NGGI, explained in Named Graph Based Global Inference (NGGI)).

inf_ext_user_func_name

The name of a user-defined inference function, or a comma-delimited list of names of user-defined functions. For information about creating user-defined inference functions, including format requirements and options for certain parameters, see API Support for User-Defined Inferencing. (For information about user-defined inferencing, including examples, see User-Defined Inferencing and Querying.)

ols_ladder_inf_lbl_sec

network_owner

Owner of the RDF network. (See Table: network_owner and network_name Parameters.)

network_name

Name of the RDF network. (See Table: network_owner and network_name Parameters.)

Usage Notes

For the inf_components_in parameter, you can specify any combination of the following keywords: SCOH, COMPH, DISJH, SYMMH, INVH, SPIH, MBRH, SPOH, DOMH, RANH, EQCH, EQPH, FPH, IFPH, DOM, RAN, SCO, DISJ, COMP, INV, SPO, FP, IFP, SYMM, TRANS, DIF, SAM, CHAIN, HASKEY, ONEOF, INTERSECT, INTERSECTSCOH, MBRLST, PROPDISJH, SKOSAXIOMS, SNOMED, SVFH, THINGH, THINGSAM, UNION, RDFP1, RDFP2, RDFP3, RDFP4, RDFP6, RDFP7, RDFP8AX, RDFP8BX, RDFP9, RDFP10, RDFP11, RDFP12A, RDFP12B, RDFP12C, RDFP13A, RDFP13B, RDFP13C, RDFP14A, RDFP14BX, RDFP15, RDFP16, RDFS2, RDFS3, RDFS4a, RDFS4b, RDFS5, RDFS6, RDFS7, RDFS8, RDFS9, RDFS10, RDFS11, RDFS12, RDFS13. For an explanation of the meaning of these keywords, see Table: Inferencing Keywords for inf_components_in Parameter, where the keywords are listed in alphabetical order.

The default set of inference components for the OWLPrime vocabulary includes the following: SCOH, COMPH, DISJH, SYMMH, INVH, SPIH, SPOH, DOMH, RANH, EQCH, EQPH, FPH, IFPH, SAMH, DOM, RAN, SCO, DISJ, COMP, INV, SPO, FP, IFP, SYMM, TRANS, DIF, RDFP14A, RDFP14BX, RDFP15, RDFP16. However, note the following:

Table: Inferencing Keywords for inf_components_in Parameter

Keyword Explanation
CHAIN Captures the property chain semantics defined in OWL 2. Only chains of length 2 are supported. By default, this is included in the SKOSCORE rulebase. Subproperty chaining is an OWL 2 feature, and for backward compatibility this component is not by default included in the OWLPrime rulebase. (For information about property chain handling, see Property Chain Handling.) (New as of Release 11.2.)
COMPH Performs inference based on owl:complementOf assertions and the interaction of owl:complementOf with other language constructs.
DIF Generates owl:differentFrom assertions based on the symmetricity of owl:differentFrom.
DISJ Infers owl:differentFrom relationships at instance level using owl:disjointWith assertions.
DISJH Performs inference based on owl:disjointWith assertions and their interactions with other language constructs.
DOM Performs inference based on RDFS2.
DOMH Performs inference based on rdfs:domain assertions and their interactions with other language constructs.
EQCH Performs inference that are relevant to owl:equivalentClass.
EQPH Performs inference that are relevant to owl:equivalentProperty.
FP Performs instance-level inference using instances of owl:FunctionalProperty.
FPH Performs inference using instances of owl:FunctionalProperty.
HASKEY Covers the semantics behind “keys” defined in OWL 2. In OWL 2, a collection of properties can be treated as a key to a class expression. For efficiency, the size of the collection must not exceed 3. (New as of Release 11.2.)
IFP Performs instance-level inference using instances of owl:InverseFunctionalProperty.
IFPH Performs inference using instances of owl:InverseFunctionalProperty.
INTERSECT Handles the core semantics of owl:intersectionOf. For example, if class C is the intersection of classes C1, C2 and C3, then C is a subclass of C1, C2, and C3. In addition, common instances of all C1, C2, and C3 are also instances of C. (New as of Release 11.2.)
INTERSECTSCOH Handles the fact that an intersection is the maximal common subset. For example, if class C is the intersection of classes C1, C2, and C3, then any common subclass of all C1, C2, and C3 is a subclass of C. (New as of Release 11.2.)
INV Performs instance-level inference using owl:inverseOf assertions.
INVH Performs inference based on owl:inverseOf assertions and their interactions with other language constructs.
MBRLST Captures the semantics that for any resource, every item in the list given as the value of the skos:memberList property is also a value of the skos:member property. (See S36 in the SKOS detailed specification.) By default, this is included in the SKOSCORE rulebase. (New as of Release 11.2.)
ONEOF Generates classification assertions based on the definition of the enumeration classes. In OWL, class extensions can be enumerated explicitly with the owl:oneOf constructor. (New as of Release 11.2.)
PROPDISJH Captures the interaction between owl:propertyDisjointWith and rdfs:subPropertyOf. By default, this is included in SKOSCORE rulebase. propertyDisjointWith is an OWL 2 feature, and for backward compatibility this component is not by default included in the OWLPrime rulebase. (New as of Release 11.2.)
RANH Performs inference based on rdfs:range assertions and their interactions with other language constructs.
RDFP* (The rules corresponding to components with a prefix of RDFP can be found in Completeness, decidability and complexity of entailment for RDF Schema and a semantic extension involving the OWL vocabulary, by H.J. Horst.)
RDFS2, … RDFS13 RDFS2, RDFS3, RDFS4a, RDFS4b, RDFS5, RDFS6, RDFS7, RDFS8, RDFS9, RDFS10, RDFS11, RDFS12, and RDFS13 are described in Section 7.3 of RDF Semantics (http://www.w3.org/TR/rdf-mt/). Note that many of the RDFS components are not relevant for OWL inference.
SAM Performs inference about individuals based on existing assertions for those individuals and owl:sameAs.
SAMH Infers owl:sameAs assertions using transitivity and symmetricity of owl:sameAs.
SCO Performs inference based on RDFS9.
SCOH Generates the subClassOf hierarchy based on existing rdfs:subClassOf assertions. Basically, C1 rdfs:subClassOf C2 and C2 rdfs:subClassOf C3 will infer C1 rdfs:subClassOf C3 based on transitivity. SCOH is also an alias of RDFS11.
SKOSAXIOMS Captures most of the axioms defined in the SKOS detailed specification. By default, this is included in the SKOSCORE rulebase. (New as of Release 11.2.)
SNOMED Performs inference based on the semantics of the OWL 2 EL profile, which captures the expressiveness of SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms), which is one of the most expressive and complex medical terminologies. (New as of Release 11.2.)
SPIH Performs inference based on interactions between rdfs:subPropertyOf and owl:inverseOf assertions.
SPO Performs inference based on RDFS7.
SPOH Generates rdfs:subPropertyOf hierarchy based on transitivity of rdfs:subPropertyOf. It is an alias of RDFS5.
SVFH Handles the following semantics that involves the interaction between owl:someValuesFrom and rdfs:subClassOf. Consider two existential restriction classes C1 and C2 that both use the same restriction property. Assume further that the owl:someValuesFrom constraint class for C1 is a subclass of that for C2. Then C1 can be inferred as a subclass of C2. (New as of Release 11.2.)
SYMM Performs instance-level inference using instances of owl:SymmetricProperty.
SYMH Performs inference for properties of type owl:SymmetricProperty.
THINGH Handles the semantics that any defined OWL class is a subclass of owl:Thing. The consequence of this rule is that instances of all defined OWL classes will become instances of owl:Thing. The size of the inferred graph will very likely be bigger with this component selected. (New as of Release 11.2.)
THINGSAM Handles the semantics that instances of owl:Thing are equal to (owl:sameAs) themselves. This component is provided for the convenience of some applications. Note that an application does not have to select this inference component to figure out an individual is equal to itself; this kind of information can easily be built in the application logic. (New as of Release 11.2.)
TRANS Calculates transitive closure for instances of owl:TransitiveProperty.
UNION Captures the core semantics of the owl:unionOf construct. Basically, the union class is a superclass of all member classes. For backward compatibility, this component is not by default included in the OWLPrime rulebase. (New as of Release 11.2.)

To deselect a component, use the component name followed by a minus (-) sign. For example, SCOH- deselects inference of the subClassOf hierarchy.

For the options parameter, you can enable the following options to override the default inferencing behavior:

For the delta_in parameter, inference performance is best if the value is small compared to the overall size of those RDF graphs. In a typical scenario, the best results might be achieved when the delta contains fewer than 10,000 triples; however, some tests have shown significant inference performance improvements with deltas as large as 100,000 triples.

For the label_gen parameter, if you want to use the default OLS label generator, specify the appropriate SEM_RDFSA package constant value from Table: SEM_RDFSA Package Constants for label_gen Parameter.

Table: SEM_RDFSA Package Constants for label_gen Parameter

Constant Description
SEM_RDFSA.LABELGEN_SUBJECT Label generator that applies the label associated with the inferred triple’s subject as the triple’s label.
SEM_RDFSA.LABELGEN_PREDICATE Label generator that applies the label associated with the inferred triple’s subject as the triple’s label.
SEM_RDFSA.LABELGEN_OBJECT Label generator that applies the label associated with the inferred triple’s subject as the triple’s label.
SEM_RDFSA.LABELGEN_RULE Label generator that applies the label associated with the rule that directly produced the inferred triple as the triple’s label. If you specify this option, you must also specify PROOF=T in the options parameter.
SEM_RDFSA.LABELGEN_DOMINATING Label generator that computes a dominating label of all the available labels for the triple’s components (subject, predicate, object, and rule), and applies it as the label for the inferred triple.

Fine-Grained Access Control (OLS) Considerations

When fine-grained access control is enabled for the entire network using OLS, only a user with FULL access privileges to the associated policy may create an inferred graph. When OLS is enabled, full access privileges to the OLS policy are granted using the SA_USER_ADMIN.SET_USER_PRIVS procedure.

Inferred triples accessed through generated labels might not be same as conceptual triples inferred directly from the user accessible triples and rules. The labels generated using a subset of triple components may be weaker than intended. For example, one of the antecedents for the inferred triple may have a higher label than any of the components of the triple. When the label is generated based on just the triple components, end users with no access to one of the antecedents may still have access to the inferred triple. Even when the antecedents are used for custom label generation, the generated label may be stronger than intended. The inference process is not exhaustive, and information pertaining to any alternate ways of inferring the same triple is not available. So, the label generated using a given set of antecedents may be too strong, because the user with access to all the triples in the alternate path could infer the triple with lower access.

Even when generating a label that dominates all its components and antecedents, the label may not be precise. This is the case when labels considered for dominating relationship have non-overlapping group information. For example, consider two labels L:C:NY and L:C:NH where L is a level, C is a component and NY and NH are two groups. A simple label that dominates these two labels is L:C:NY,NH, and a true supremum for the two labels is L:C:US, where US is parent group for both NY and NH. Unfortunately, neither of these two dominating labels is precise for the triple inferred from the triples with first two labels. If L:C:NY,NH is used for the inferred triple, a user with membership in either of these groups has access to the inferred triple, whereas the same user does not have access to one of its antecedents. On the other hand, if L:C:US is used for the inferred triple, a user with membership in both the groups and not in the US group will not be able to access the inferred triple, whereas that user could infer the triple by directly accessing its components and antecedents.

Because of these unique challenges with inferred triples, extra caution must be taken when choosing or implementing the label generator.

See also the OLS example in the Examples section.

For information about RDF network types and options, see RDF Networks.

Note: If the SEM_APIS.CREATE_INFERRED_GRAPH procedure with OWL2RL reasoning takes a long time to execute , then the create inferred graph procedure needs to be executed with options as shown for the OWL2RL rulebase example in the Examples section.

Examples

The following example creates an inferred graph named OWLTST_IDX using the OWLPrime rulebase, and it causes proof to be generated for inferred triples.

EXECUTE sem_apis.create_inferred_graph('owltst_idx', sem_models('owltst'), sem_rulebases('OWLPRIME'), SEM_APIS.REACH_CLOSURE, null, 'PROOF=T');

The following example assumes an OLS environment. It creates a rulebase with a rule, and it creates an inferred graph.

-- Create an inferred graph with a rule. --
exec sdo_rdf_inference.create_inferred_graph('contracts_rb');

insert into rdfr_contracts_rb values (
  'projectLedBy', '(?x :drivenBy ?y) (?y :hasVP ?z)',  NULL,
  '(?x :isLedBy ?z)',
  SDO_RDF_Aliases(SDO_RDF_Alias('','http://www.myorg.com/pred/')));

-- Assign sensitivity label for the predicate to be inferred. --
-- Yhe predicate label may be set globally or it can be assign to --
-- the one or the RDF graphs used to infer the data - e.g: CONTRACTS.
begin
  sem_rdfsa.set_predicate_label(
         model_name   => 'rdf$global',
         predicate    => 'http://www.myorg.com/pred/isLedBy',
         label_string => 'TS:US_SPCL');
end;
/

-- Create index with a specific label generator. --
begin
  sem_apis.create_inferred_graph(
         inferred_graph_name      => 'contracts_inf',
         rdf_graphs_in            => sem_models('contracts'),
         rulebases_in             => sem_Rulebases('contracts_rb'),
         options                  => 'USER_RULES=T',
         label_gen                => sem_rdfsa.LABELGEN_PREDICATE);
end;
/

-- Check for any label exceptions and update them accordingly. --
update rdfi_contracts_inf set ctxt1 = 1100 where ctxt1 = -1;

-- The new inferred graph is now ready for use in SEM_MATCH queries. --

The following example shows the steps to overcome long execution time when creating inferred graphs with OWL2RL rulebase.

ALTER SESSION SET "_OPTIMIZER_GENERATE_TRANSITIVE_PRED"=FALSE;
EXECUTE SEM_APIS.CREATE_INFERRED_GRAPH
  ('m1_inf',SEM_MODELS('m1'),SEM_RULEBASES('OWL2RL'),NULL,NULL,
   'RAW8=T,DOP=8,HINTS=[rule:SCM-CLS,use_hash(m1),rule:SCM-OP-DP,use_hash(m1)],PROCSVF=F,PROCAVF=F,PROCSCMHV=F,PROCSVFH=F,PROCAVFH=F,PROCDOM=F,PROCRAN=F'
  );