Oracle Enterprise Data Quality for Product Data Knowledge Studio Reference Guide, Release 11g R1 (11.1.1.6)

Release 11g R1 (11.1.1.6)

E29134-02

Contents

Title and Copyright Information

Preface

- Audience

- Documentation Accessibility

- Related Documents

- Conventions

1 Overview

- Starting the Software

- Understanding the Client Workspace

- Frame Functionality

- Menu Commands and the Toolbar

- File Menu

- Edit Menu

- View Menu

- Data Lens Menu

- Tools Menu

- Help Menu

- Keyboard Shortcuts

- Tabs and Sub-Tabs

- Task Panes

- Context-Sensitive Menus

- Starting the Knowledge Studio

- Creating or Opening a Data Lens

- Setting Knowledge Studio Options

2 Phrases in Data

- Phrases Tab

- Define Phrases Sub-Tab

- Phrases and Terms Pane

- Sample Data Table pane

- Item Definition Field

- Graphical Rule Builder Pane

- Selected Line Field

- Using These Tools

- Creating Terminology Rules for Unknown Items

- Creating Phrase Structure for Terminology Nodes

- Combining Two Phrase Rules into One Phrase Rule

- Merging Two Phrase Rules

- Renaming Phrase Rules

- Editing Phrase Rules

- Adding Variant Forms of Terminology

- Modifying Phrase Productions

- Using Rule Predictions

- Resolving Phrase Ambiguity

- Editing the Term Full Form

- Define Items Sub-Tab

- Item Definitions Pane

- Sample Data Table Pane

- Item Definition Field and Selected Line Field

- Graphical Rule Builder Pane

- Using these Tools

- Creating a New Top-Level Item Definition

- Creating a Sub-Item Definition

- Aliases

- Active vs. Inactive Item Definitions

- Item Definition List Pane

- Using these Tools

- Defining Item Definition Attributes

- View Hierarchy Sub-Tab

- Top Down and Bottom Up Grammar Hierarchy Panes

- Regression Testing Sub-Tab

- Ensuring Regression Testing is Active

- Before Pane

- After Pane

- Review Column

- Creating and Updating the Regression Base

3 Standardize Data

- Standardize Tab

- Standardize Terms Sub-Tab

- Terminology Rules Pane

- Rewrite Rules Pane

- Copy Full Form Button

- Default Case Section

- Standardize Phrases Sub-Tab

- Nodes to Receive Rules Pane

- Sort Productions

- Nodes for Insertion Pane

- Ordering Rule Pane

- Join Terms

- Delete or Reorder Terms

- Standardize Lines Sub-Tab

- Unit Conversion Sub-Tab

- Nodes to Receive Rules Pane

- Instructions Pane

- Unit Conversion Table Construction Wizard Pane

- Test Global Standardization Sub-Tab

- Sample Data Table

- Sub-Tab Data Fields

- Regression Test Sub-Tab

- Before Pane

- After Pane

- Review Column

- Creating and Updating the Regression Base

- Standardization Types

- Creating a Standardization Type

- Deleting Standardization Types

4 Standardizing Item Definitions

- Standardize Items Tab

- Standardize Attributes Sub-Tab

- Item Definitions Pane

- Rewrite Rules Pane

- Numeric Rewrite Rules

- Value Replace Table

- Simple Rewrite Rules

- Automatic Rewrite Rules

- Reorder Phrase Productions

- Order Attributes Sub-Tab

- Item Definition Pane

- Selection Pane

- Null Handling

- Multiple Instance Handling

- Match Weights Sub-Tab

- Item Definition Pane

- Selection Pane

- Duplicate Matching

- Test Attributes Sub-Tab

- Item Definition Section

- Sample Data Table

- Source Field

- Standardized Text Section

- Standardized Attributes Table

- Test Item Standardization Sub-Tab

- Sample Data Table

- Source Field

- Standardized Text Section

- Item Attributes Section

- Regression Test Sub-Tab

- Sample Data Table

- Current and Regression Attributes Sections

- Creating and Updating the Regression Base

- Match Type

5 Classify Data

- Classification Schemas

- UNSPSC

- eCl@ss

- User-Defined

- Classification Type

- Creating a Classification Type

- Deleting Classification Types

- Classify Tab

- Classify from Data Sub-Tab

- Classification Type Pane

- Sample Data Pane

- Item Definition Section

- Graphical Rule Pane

- Classify from Item Definitions Sub-Tab

- Classify from Rules Sub-Tab

- Test Classification Sub-Tab

- Sample Data Pane

- Source Field

- Standardized Text Field

- Classification Pane

- Testing Multi-Classification

- Regression Test Sub-Tab

- Creating and Updating the Regression Base

6 Translating Data

- Translation Process

- Data Lens Standardizations Quality

- Prepare Data for Translation

- Translation Target

- Translation Smart Glossary

- Create/Update Oracle DataLens Server Translation Smart Glossary

- Translation Tab

- New Phrases and Known Phrases Sub-Tabs

- Translation Pane

- Red Check Mark

- Export

- Export Text for Translation

- Import Text for Translation

- Source and Translated Text Fields

- New and Known Variable Term Phrases Sub-Tabs

- Reorder Sub-Tab

- Test Translation Sub-Tabs

- Test Translated Attributes Sub-Tab

- Item Definition Section

- Sample Data Table

- Source Field

- Translated Text Section

- Standardized Attributes Table

- Test Item Translation Sub-Tab

- Sample Data Table

- Source Field

- Item Definition Field

- Test Global Translation Sub-Tab

- Sample Data Table

- Source Field

- Translated Field

- Quality Metrics

- Regression Test Sub-Tab

- Before Pane

- After Pane

- Review Column

- Creating and Updating the Regression Base

- Translation Repair Formats

7 Generating Reports

- Function Specific Reports

- Classification Reports

- Standardization Report

- Grammar Reports

- Complexity Reports

- Semantic Reports

8 Managing Data Lenses and Files

- Data Lens Management

- Setting Data Lens Options

- Checking In a Data Lens

- Checking Out a Data Lens

- Deleting Data Lenses

- Deleting Read-only Data Lenses

- Changing Data Lens Information

- Editing a Data Lens Description

- Editing Data Lens History Notes

- Viewing Tasks

- Changing the Task Status

- Viewing Data Enrichment Tasks

- Creating a Task

- Sample Files

- Creating New Sample Data

- Deleting Sample Files

- Renaming Sample Files

- Combining Sample Files

9 Export and Import Features

- Exporting a Data Lens

- Importing a Data Lens

- Exporting Rules

- Importing Phrases and Terms

- Creating Aliases When Importing Rules and Phrases Using Excel

- Exporting and Importing Item Definitions

- Troubleshooting Item Definition Export and Import

- Pre-existing Item Definitions with Same Name as Import

- Source Item Definition Relies on Knowledge Contained in an Smart Glossary

- Source Item Definition Relies on Standardization Information

- Source Item Definition Relies on Unit Conversion Information

- Source Item Definition Relies on Value Logic Information

- Importing Enrichments

- Importing from Your Oracle DataLens Server

- Importing from a Downloaded File

10 Defining Context and Item Definitions Further

- Editing Multiple Phrases and Terms

- Generating Term Variants

- Phrase and Terminology Rule Syntax

- Terminology Rules

- Case Sensitivity in Terminology Rules

- Regular expressions in Terminology Rules

- White Space, Regular Expressions, and Terminology Rules

- Phrase Structure Rules

- Start Symbols in Phrase Structure Rules

- Other Constraints on Rules

- Global Phrase Rule Renaming

- Source Format

- Applying Source Formatting

- Compacting Grammar

11 Standardizing Data Further

- Term Standardization

- Case Replacement

- Regular Expression Replacement

- Individual Replacement

- Multiple Standardization Types

- Unit of Measure Standardization Types

- Creating a Unit Conversion Type

- Deleting Unit Conversion Types

- Creating a Unit Conversion for a Phrase

- Share Standardizations Within a Data Lens

- Copying Global Standardizations

- Sharing Item Definitions Standardizations

- Copying Item Definitions Between Standardization Types

- Copying Between Item Definitions

12 Classifying Data Further

- Advanced Classification Rule Functions

- Addition

- Masking

- Negation

- Parent

- Function Example

- Add Mask

- Add Negation

- Add a Parent

- Classifying to Several Schemas in a Data Lens

- Upgrading a Classification Type

- Creating User-Defined Classification Types

- Creating a User-Defined Schema Using Excel

- Creating a User-Defined Schema Using a Text Editor

- Creating a User-Defined Classification Type

- Global Classification Schemas

- Classification System Support

13 Smart Glossaries

- Importing a Smart Glossary

- Creating a Smart Glossary

- Included Smart Glossaries

- Colors

- Counts

- Materials and Finishes

- Product Packaging

- Units of Measure

- Units of Measure Retail

- Using Translated Smart Glossaries

A Regular Expressions

- Special Characters in Regular Expressions

- Useful Regular Expressions in Terminology Rules

Oracle® Enterprise Data Quality for Product Data

Knowledge Studio Reference Guide

Release 11g R1 (11.1.1.6)

E29134-02

February 2013

Oracle Enterprise Data Quality for Product Data Knowledge Studio Reference Guide, Release 11g R1 (11.1.1.6)

E29134-02

Copyright © 2001, 2013,  Oracle and/or its affiliates. All rights reserved.

Primary Author:  Lorna Vallad

This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited.

The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing.

If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, the following notice is applicable:

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This software or hardware and documentation may provide access to or information on content, products, and services from third parties. Oracle Corporation and its affiliates are not responsible for and expressly disclaim all warranties of any kind with respect to third-party content, products, and services. Oracle Corporation and its affiliates will not be responsible for any loss, costs, or damages incurred due to your access to or use of third-party content, products, or services.

Preface

This reference guide is intended to explain the basic capabilities of the Oracle Enterprise Data Quality for Product Data Knowledge Studio. The document is organized as follows:

- Chapters 1 through 9 describe the basic application features.

- Chapters 10 through 13 describe more advanced features and functionality.

To understand all of the advanced features presented, you must use this reference guide in conjunction with the Oracle Enterprise Data Quality for Product Data documents listed in "Related Documents".

Review the following Oracle Enterprise Data Quality for Product Data documentation prior to the use of this manual is recommended:

- Oracle Enterprise Data Quality for Product Data Application Studio Reference Guide

- Oracle Enterprise Data Quality for Product Data Governance Studio Reference Guide

Audience

You should have a basic understanding of the DataLens Technology, including the functionality of both the Oracle Enterprise Data Quality for Product Data Application Studio and Governance Studio.

This document is intended for all users of the DataLens Technology, including:

- Business Analysts

- Subject Matter Experts (SMEs)

- IT Administrators

- Application/Solution Owners

Documentation Accessibility

For information about Oracle's commitment to accessibility, visit the Oracle Accessibility Program website at http://www.oracle.com/pls/topic/lookup?ctx=acc&id=docacc.

Access to Oracle Support

Oracle customers have access to electronic support through My Oracle Support. For information, visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=info or visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=trs if you are hearing impaired.

Related Documents

For more information, see the following documents in the documentation set:

- The Oracle Enterprise Data Quality for Product Data Getting Started Guide provides information about how to get started with EDQP and use the Application Launch Pad to start client applications.

- The Oracle Enterprise Data Quality for Product Data Application Studio Reference Guide provides information about creating and maintaining Data Service Applications (DSAs).

- The Oracle Enterprise Data Quality for Product Data AutoBuild Reference Guide provides information about creating an initial data lens based on existing product information and data lens knowledge.

- The Oracle Enterprise Data Quality for Product Data Governance Studio Reference Guide provides information about building projects to analyze your transformed data, create reports to show the quality of your data, and identify missing attributes.

See the latest version of this and all documents in the Oracle Enterprise Data Quality for Product Data Documentation Web site at

http://docs.oracle.com/cd/E35636_01/index.htm

Conventions

The following text conventions are used in this document:

Convention

Meaning

boldface

Boldface type indicates graphical user interface elements associated with an action, or terms defined in text or the glossary.

italic

Italic type indicates book titles, emphasis, or placeholder variables for which you supply particular values.

monospace

Monospace type indicates commands within a paragraph, URLs, code in examples, text that appears on the screen, text that you enter, or a file, directory, or path name.

monospace

Boldface, monospace type indicates commands or text that you enter.

1 Overview

This guide describes basic and advanced techniques that you can use to maximize the effectiveness of the Enterprise DQ for Product (EDQP) Knowledge Studio. These techniques help refine your knowledge about your data and supply your Subject Matter Experts (SME) with in-depth information on important aspects of the DataLens methodology.

The Knowledge Studio allows you to create data lenses, which are collections of rules that enable the recognition, classification, and standardization of data. There are three main activities required to build a data lens:

Recognition of the data:

Create rules to recognize the data and build variant forms into the lens.

Definition of the items:

Identify the attributes necessary to accurately define an item.

Standardization of the data:

Create standardization rules for terms, phrases, and Item Definitions.

This reference guide will help you understand the process of building a data lens using writing instruments product data.

Starting the Software

You start your Oracle DataLens Server, and then use the Welcome Launch Pad to start the Knowledge Studio by clicking the Knowledge Studio button. For details, see Oracle Enterprise Data Quality for Product Data Getting Started.

Understanding the Client Workspace

The Knowledge Studio graphical user interface (GUI) provides the client workspace used to create and manage a data lens.

- "Frame Functionality"

- "Menu Commands and the Toolbar"

- "Tabs and Sub-Tabs"

- "Task Panes"

Note:
Functionality that has not been configured or that the current user is not authorized to use is dimmed.

Frame Functionality

The Knowledge Studio client workspace frame contains useful information and interactive functions including the following:

Title Bar

Indicates the current application and open project.

Status Field

Provides the processing status of the data lens one line at a time. This field can be resized and the scroll arrows on the right-hand side can be used to view all available status information. The status data does not change based on the selected tab, rather it is a compilation of all data.

Status Field View

Controls whether the Status Field is displayed or not.

Application Switch

Returns you to the last Enterprise DQ for Product application used.

Oracle Enterprise Data Quality for Product Data Launch Pad

This button opens the Oracle Enterprise Data Quality for Product Data Launch Pad so that you can select other applications.

Time and Date

The time is displayed and when you hover over this field, the date displays.

Memory Cache

Indicates the amount of memory cache currently used and the total amount allowed. You can dump the memory cache by clicking on the trash can icon in this interactive field.

Note:
This feature is only used for system diagnosis and should not be used unless requested by the support team.

Menu Commands and the Toolbar

The Knowledge Studio toolbar allows easy access to the most frequently used Knowledge Studio functions. Though the set of toolbar buttons remains the same during user interface operation the buttons are enabled or disabled based the current state of you interface and options set. Buttons displayed with shades of gray are disabled. Full-color buttons are enabled. All toolbar buttons are standard push buttons, requiring a single click of the mouse to activate.

The following briefly describes the toolbar buttons from left to right.

The Knowledge Studio GUI menus provide access to most Knowledge Studio functions. All of the buttons on the toolbar have a corresponding menu command, which are indicated on each menu with the button icon displaying adjacent to the command. The set of menu commands remains the same during the GUI operation.

Menu commands are enabled or disabled based on the current state of the data lens; commands that are dimmed are unavailable. Some menu commands perform functions that are more complex and are indicated by an ellipsis symbol (...). These commands open dialogs to collect information needed to complete the requested function. Menu commands that toggle user functions are preceded by check mark.

Tip:
The tooltips appear when you rest your mouse pointer on a menu item, button, tab, icon, or similar content.

The following sections briefly describe each of the Knowledge Studio menu commands and corresponding buttons.

File Menu

New Data Lens…
Creates a new data lens file for processing data. Data lens files are stored in the following directory:

C:\Documents and Settings\Username\Applications\DataLens\data\project

or

C:\Users\Username\AppData\Roaming\DataLens\project

Open Data Lens…

Opens an existing data lens file and closes any open data lens file.

Recent Lens

Provides a list of recently opened data lens for you to select from so that you can quickly open your data lenses.

Select Data File

Opens a sample data file associated with the current data lens and closes the currently open sample data file.

Close Data Lens

Closes the open data lens file.

Save

Saves all contextual changes to disk and creates a version of the data lens that you can revert to.Save As

Allows you to save the current data lens to a new name.

Delete Data Lens

Allows you to delete the open data lens from your local

machine. A warning message is displayed prior to deletion. Only the local copy of the data lens is deleted. If you checked in the data lens into the server, that copy is still present on the server and must be deleted from the server. See "Deleting Data Lenses".

Delete Read-Only Lenses…
Allows you to delete any unwanted 'read only' data lens from your local machine. See "Deleting Read-only Data Lenses".

Delete Sample Files…
Allows you to delete the sample files associated with the data lens that you are currently editing. You can designate 'All' or a specific sample file for deletion. See "Deleting Sample Files".

Update Regression Base
Allows you to update the current regression testing base based on contextual changes in the tab currently open.

Create New Regression Base
Creates a new regression base file, which identifies the effects of your changes as changes are made to terminology and phrases.

Reports
Allows you to select a report formats for viewing results. See "Complexity Reports".

Complexity Reports
Allows you to select a report that shows the complexity of the data. See "Complexity Reports".

Semantic Reports
Allows you to select a report that counts the parsed phrase context of the data within the selected data lens. See "Semantic Reports".

Export Phrases for Translation
Exports phrases from the translation dictionary. See "Translation Tab".

Import Translated Phrases
Imports phrases into the translation dictionary.

Import Current/All Translated Phrases
Imports some or all phrases. See "Translation Tab".

Create/Update Glossary
Allows you to create or update a translation glossary on the Oracle DataLens Server.

Export Data Lens
Exports the entire data lens and creates a data lens export file project directory:

\Documents and Settings\Username\Application Data\DataLens\export\data lens name

See "Exporting a Data Lens".

Import Data Lens
Imports an exported data lens from the specified export directory. See "Importing a Data Lens".

Export Rules
Allows you to export term and phrase rules. See "Exporting Rules".

Export Rules by Domain
Allows you to export term and phrase rules by a domain. See "Exporting Rules".

Export Attributes
Allows you to export attributes (from Item Definitions) to an Excel spreadsheet file. The report provides attribute information at Item Definition level that shows Attribute Type, Attribute Alias, Attribute Name, Rules defining the attribute and the order for each Standardization.

Import Enrichments from server
Allows you to import data enrichment knowledge created in Governance Studio into your data lens directly from your Oracle DataLens Server. See "Importing from a Downloaded File".

Import Enrichments File
Allows you to import data enrichment knowledge created in Governance Studio into your data lens from a file you download from a task. See "Importing from a Downloaded File".

Import Phrases and Terms
Allows you to import knowledge (phrase rules, terminology rules, and term variants) into a data lens from an Excel spreadsheet or a tab-delimited file. See "Importing Phrases and Terms".

Import Item Definitions
Allows you to import Item Definitions into a data lens from a tab-delimited file. See "Exporting and Importing Item Definitions".

Import Smart Glossaries
Allows you to import foundation data lenses to your current data lens. See "Importing a Smart Glossary".

New Sample Data
Allows you to create new sample data files to add to the existing set of samples. See "Sample Files".

Rename Sample Files…
Allows you to rename existing sample files associated with the data lens. See "Sample Files".

Combine sample data
Allows you to combine selected sample files into a single file to be used for regression testing. See "Sample Files".

Revert to prior Data Lens
Allows you to revert to a previous version of the current data lens. The data lenses that are listed are local copies only and are not the Oracle DataLens Server.

Exit

Exits the Knowledge Studio application; a prompt is given for unsaved changes.

Edit Menu

Cut
Deletes the selection and copies it to the clipboard.

Copy

Copies the selection to the clipboard.

Paste

Pastes contents of the clipboard at the current insertion point.

Replace

Searches for and replaces the specified text on the Translate tab.

Rename Rules
Allows you to globally rename phrase rules to consolidate them. This feature is only available on the Define Phrase sub-tab of the Phrases tab. See "Global Phrase Rule Renaming".

Move Rules

Allows you to drag and drop rules across Domains (folders) in the hierarchical folder style Move Rules dialog. For example, you can move a rule from a Smart Glossary into the phrase structure of your data lens.

Delete Unused Terms
Allows you to delete unused terms. An unused term is a term that is not referenced by any rules or phrases. It is denoted by the purple ball with a "u" inside icon.

Edit Attributes Aliases…
Allows you to edit the attribute aliases of phrases and terminology. See "Aliases".

Edit Phrase and Term Attributes…
Allows you to edit the attributes of phrases and terminology. See "Editing Multiple Phrases and Terms".

Edit Lens Description…
Allows you to modify the data lens description. See "Editing a Data Lens Description".

Edit History Notes
Allows you to enter text regarding the data lens maintenance to provide an audit trail for ongoing support. If Foundation or Domains are imported into the data lens, this information is included with a date and timestamp. See "Editing Data Lens History Notes".

Find…
Allows you to specify a search string (regular expression) and attempts to find it. The left-hand tree panes of the Knowledge Studio creation tabs (Phrases, Standardize, and Classify tabs) are searched.

Find Next
Repeats the last search defined by a Find operation.

Undo
Removes any changes that you have made and reverts the data lens to the last saved state.

Predict Terms
All possible rules that could apply to the input data for an individual sample row, based on confidence ratings and meeting the Prediction Threshold, are displayed for you to choose from or a message that advises you why no predictions are available. Predict Terms only works in the context of Item Definition where the sample row has an associated Item Definition. You can select the appropriate rule or reject the predictions. Rejecting predictions is only applicable to the current data lens editing session and is reset when you close the data lens.

View Menu

View My Tasks
Allows you to view any tasks that are scheduled or have run. See "Viewing Tasks".

Filter…

Allows you to filter the displayed data based on text or a text pattern. The filter operation applies only to the currently selected tab. Only the rows that match the text entered in the Filter dialog are displayed in the task pane.

Remove Filter:

Removes the filter currently applied and all data is displayed.

Refresh

Redisplays the data including changes that were just applied using the Apply function.

Show ID

Displays the ID column in tabular panes when selected; selecting again removes the column from the task pane.

Previous
Returns to the previous phrase or rule ambiguity.

Next
Advances to the next phrase or rule ambiguity.

Search Internet
Allows you to search the Internet for the text selected in the Input Data field on the Define Phrases or Define Items sub-tabs of the Phrases tab. Your default browser application is launched and a search is performed using the selected text as the search string.

Search Images
Allows you to search the Internet for the images matching the text selected in the Input Data field on the Define Phrases or Define Items sub-tabs of the Phrases tab. Your default browser application is launched and an image search is performed using the selected text as the search string.

Search Context
Allows you to search for the selected line of data so that you can select it in a different context. This feature is only available on the Translation tab.

List Regression Tests
Displays information about regression tests that are associated with selected data lens. The display will show the type of regression created and the sample file that the regression test is against.

View Lens Information
Displays specific information about the data lens and data file that is currently being used.

View Attributes for Deployed Lens
Displays attribute information about the currently deployed data lens by Item Definition including attribute use.

View Server Information
Displays server information for the Oracle DataLens Server.

View Check-In History
Lists the data lenses that you have checked in including the comments regarding the check-in.

View My Checkouts
Lists the data lenses that you have checked out.

View All Checkouts
Lists all data lenses on the Oracle DataLens Server that have been checked out.

View as Production
Displays the output data from Item Definitions set to inactive. See "Active vs. Inactive Item Definitions".

Data Lens Menu

Check-In Data Lens…
Allows you to check-in a data lens file into your Oracle DataLens Server repository. Each time you check a data lens into the Oracle DataLens Server, the data lens revision number is incremented. The Oracle DataLens Server maintains all of the previous revisions of a data lens. You can check in a data lens under one of two conditions: it has never been checked in before or it was previously checked out and locked for editing by you. The Check-In dialog allows you to enter a comment to be stored with this revision of the data lens. If you want to continue to edit the data lens, select the Keep Locked for More Editing check box so the data lens can only be checked-out by another person in a 'Read Only' mode. Selecting this option dims the Delete local Data Lens command, which removes the local copy of the data lens from your client. See "Checking In a Data Lens".

Check-Out Data Lens…
Allows you to select the data lens and the specific revision number to check out from the Oracle DataLens Server repository and automatically locks it for editing. You can also check out the data lens and assign a new name, which creates a new data lens from an existing data lens. See "Checking Out a Data Lens"

Unlock Data Lens
Unlocks the current data lens from the repository in the Oracle DataLens Server.

Copy Global Standardizations
Copies the global standardization rules from the current Standardization Type to another. See "Copying Global Standardizations".

Apply
Activates the knowledge that you have just created. This option is active only when there is knowledge you have not saved. After you apply your changes, use the Refresh command to see the effect on your sample data.

Translate
Performs the translation of phrases (Translate tab) or complete content lines (Test Translations tab). See "Translating Data".

Source Format
Allows you to edit the source formatting expressions. See "Source Format".

Standardization Repair Formats
Allows you to enter sed scripting to repair standardized data.

Translation Repair Formats
Allows you to enter sed scripting to repair translated data. See "Translation Repair Formats".

Open Excel Override File
Starts Excel with a spreadsheet that can be used to enter specific context to be used within this data lens. This feature will be deprecated and should not be used.

Compact Grammar
Allows you to remove any grammar rules that are not being utilized based on the data within the lens. See "Compacting Grammar".

Unit Conversion Types…
Allows you to add, select, and activate the Enterprise DQ for Product supplied unit conversions. Unit conversions enable the creation of output with consistent use of units. For example, your data may express resistance in ohms, kilo-ohms, and mega-ohms. With a unit conversion, consistency of output could be maintained by converting each of the preceding to ohms. See "Unit of Measure Standardization Types".

Standardization Types…
Allows you to add, select, and activate the Enterprise DQ for Product supplied unit conversions. Standardization types also allows you to create your own standardization schemas for use throughout your data lens. See "Standardization Types".

Match Types…
Allows you to add and use schemas to automatically match data. See "Match Type".

Classification Types…
Allows you to add and use schemas to automatically classify data. See "Classification Type".

Translation Targets…
Allows you to select the locales/languages for which you want data translation. This option is not available until your data lens is standardized. Activates the Translation tab. See "Translation Target".

Data Lens Options
Allows selection of the global data lens parameters including text case sensitivity, whether the data lens can be imported, and the behavior of the Apply functionality. See "Setting Data Lens Options".

Tools Menu

Open Oracle DataLens Governance Studio…
Starts the EDQP Governance Studio. See Oracle Enterprise Data Quality for Product Data Governance Studio Reference Guide.

Open Oracle DataLens Application Studio…

Starts the EDQP Application Studio. See Oracle Enterprise Data Quality for Product Data Application Studio Reference Guide.

Open Oracle DataLens Task Manager…
Starts the EDQP Task Manager. See Oracle Enterprise Data Quality for Product Data Task Manager Guide.

Open Oracle Enterprise Data Quality for Product Data Launch Pad…

Starts the Oracle Enterprise Data Quality for Product Data Launch Pad so that you can start other applications and the Enterprise DQ for Product Oracle DataLens Server Administration Web pages.

Open Character Map…
Opens the Windows Character Map dialog to enable character mapping changes. This function is provided as a shortcut way of inserting special characters and symbols not available on the keyboard when translating phrases.

Help Menu

Product Guide
Opens a list of Enterprise DQ for Product documents for your selection in a browser.

Help About
Provides information regarding the product including the version number and a link to view third party product licenses.

Keyboard Shortcuts

The following table contains keyboard shortcuts that can help make the Knowledge Studio easier to use.

Function

Shortcut Key

Save

Ctrl+S

Undo

Ctrl+Z

Find

Ctrl+F

Find Next

F3

Apply

Alt+A

Refresh

Alt+R

Cut

Ctrl+X

Copy

Ctrl+C

Paste

Ctrl+V

Predict

Ctrl+P

New Data Lens

Ctrl+N

Open Data Lens

Ctrl+O

Tabs and Sub-Tabs

A tab groups like information into easy to read and access areas that include sub-tabs, panes, and text entry boxes. Tabs are displayed in the Workspace directly under the toolbar and can be invoked in any order. Not all tabs are available at all times. For example, the Translate tab and sub-tabs are not visible until a translation target is activated.

A sub-tab operates like a tab and provides specific functionality or utilities related to each tab and so are different for each tab.

The tabs and the related sub-tabs included in the Knowledge Studio are as follows:

Tab

Related Sub-Tabs

Phrases (Chapter 2, "Phrases in Data" )

Define Phrases
Define Items

View Hierarchy

Regression Test

Standardize (Chapter 3, "Standardize Data")

Standardize Terms
Standardize Phrases

Standardize Lines

Unit Conversion

Test Global Standardization

Regression Test

Standardize Items (Chapter 4, "Standardizing Item Definitions")

Standardize Attributes
Order Attributes

Match Weights

Test Attributes

Test Item Standardization

Regression Test

Classify (Chapter 5, "Classify Data")

Classify from Data
Classify from Item Definitions

Classify from Rules

Test Classification

Regression Test

Translate (Chapter 6, "Translating Data")

New Phrases and Known Phrases
New and Known Variable Term Phrases

Reorder

Test Translated Attributes

Test Item Translation

Test Global Translation

Regression Test

Task Panes

The interactive task panes allow you to perform actions specific to the type of pane and these actions are described throughout this reference. In general, the task panes included in the Knowledge Studio are as follows:

Hierarchical Structures

Data is represented in a tree-like structure that shows how nodes are related. You can drag and drop the nodes into other panes some though not all cases. The parent nodes can be expanded to view all related children nodes.

Forms

Data is entered into fields and options are selected to build knowledge.

Graphical Rule

Data is represented with graphical icons that you can drag and drop to change it.

Tabular

Data is displayed in tabular form similar to a Microsoft Excel spreadsheet.

Wizards

Data is collected via queries in a step-by-step manner.

Arrows

The small up/down arrows between the panes, allow you to resize the panes. In addition, you can fully expand either pane to see more data by clicking on an arrow, which makes the pane inactive. To redisplay the inactive pane, click the opposite arrow and the pane reappears.

Context-Sensitive Menus

There are various context-sensitive (shortcut) menus that appear in the Knowledge Studio panes when you right-click on data within a task pane. The contents of these menus are described throughout this reference though may contain the following standard options:

Filter and Un-Filter

These options filter and un-filter data as previously described.

Icon Help

Explains each of the icons that can appear and is context-sensitive.

Expand Node

Expands all sub-nodes (phrases or terms) of the selected node in a hierarchical manner.

Expand All

Expands all sub-nodes (phrases or terms) of the selected node in a hierarchical manner.

Find and Find Again

Locates text data as previously described.

Remove Category Visually

Removes the selected category from displaying in the pane. The categories are only removed for the current data lens editing session and are reset when you close the data lens.

Search Internet

Searches the Internet for the selected text, which appears as part of the menu selection name.

Starting the Knowledge Studio

If this is the first time you have started the Knowledge Studio, the client workspace appears blank as in the following figure; otherwise, the results from the last job run are displayed.

The status field at the bottom of the Knowledge Studio client workspace provides information about any data lenses you load in the white field, and the date and time, and memory usage are displayed in the grey fields. The status field is blank until you have created your first Knowledge Studio project, at which time the status of your project is displayed. See "Understanding the Client Workspace".

Creating or Opening a Data Lens

When you launch the Knowledge Studio, you are prompted to select an existing data lens to open.

1. Since you are starting a new data lens, click Cancel to close this dialog. From the File menu, click New Data Lens data lens create your new data lens.

2. Enter the unique name for this data lens.

Note:
Entering a space results in an underscore.

3. Enter a description and select a Character Encoding from the list.

4. Click on the Select button adjacent to the Data Source field to select the file that contains your data. The Data Source dialog appears.

5. Select the MS Excel file option and click the Specify button.

6. Click Browse, locate your data file, and then select it.
The Excel file field names are displayed.

7. Select the Id field and click on the right arrow to populate the ID list, and then select the Description field to populate the Description list. The ID corresponds to a part number field in the Excel spreadsheet; the Description is a description of a part, including the item name and several attributes.

8. Click OK. The Data Source dialog appears, indicating the source file that you specified and the number of lines of data.

9. Click OK.
You are returned to the New Data Lens dialog.

10. Click OK. The Knowledge Studio creates your new data lens, including a set of sample files. These sample files are XML representation of the data in your Excel spreadsheet.
Your new project is located in:

C:\Documents and Settings\Username\Applications\DataLens\data\project

or

C:\Users\Username\AppData\Roaming\DataLens\project

Your content and sample files are located in:

…\data lens name\inputData

The sample files have the .xml file extension.

11. When prompted to select a sample data file, click Browse.

12. Select your sample data file, and click Open.
Your data lens opens and is now ready for use.

Setting Knowledge Studio Options

There are various global options that you can set to configure how the Knowledge Studio operates. From the Tools menu, select Options. Select the global application options as follows:

Parse Tree Node Font Size
Allows you to select the font size you want for the display of phrase trees in the Graphical Rule Editor pane on the Define Phrases tab. A smaller font allows you to see more phrases for longer lines.

Number of Apply's before Save

Allows you to automatically save your data lens as you apply knowledge.

Number of Save's before Backup

Allows you to automatically backup your data lens after a determined number of Saves.

Maximum number of backups

Allows you to set the maximum number of data lens revisions that will be retained on your Oracle DataLens Server. The default setting is three; the maximum setting is 10. You can control how much disk space your server is using and the speed of data lens check in and check out by setting this number to a lower setting.

Ghosting Percent

Allows you to set to percentage of ghosting that will be used to display terms and phrases that are not associated with an Item Definition. Percentage can be from 10% to 100%. A lower percentage setting will result in the terms and phrases being shown lighter (more ghosted).

Two-line tool bar
Allows you to choose whether the toolbar is display on a single line or on two lines. Choosing a two-line tool bar allows you to see all of the toolbar items even when the Knowledge Studio screen is smaller than normal size.

Double-click Jump
Allows you to 'jump' or switch to between views of a selected node by double-clicking on an empty area of the pane. This functionality is context-sensitive and changes the active tab.

Show Source-Formatted Text
Enables the display of text that has been reformatted by the Source Formatting feature so that you can quickly identify this data for further standardization.

Show Predictions
Enables the textual display of the predictions for unknown data nodes. Controls whether the prediction options on the Edit menu and the Define Phrases sub-tab Graphical Rule Builder pane context-sensitive menu are active.

Enable Bidirectional Text Dialog
Activates the Bidirectional Text tab in the Data Lens Options dialog. See "Setting Data Lens Options".

2 Phrases in Data

This chapter explains how to create Terminology Rules (Term Rules) and phrase structures.

Phrases Tab

The Phrases tab and associated sub-tabs are used to build the phrase structures that describe your sample data in a data lens. The Define Phrases sub-tab is the default and is the initial sub-tab used to begin building your data lens.

Define Phrases Sub-Tab

The Knowledge Studio separates your sample data automatically. The selected line of sample data is colored yellow; in the selected line, the unrecognized text is gold, and the ambiguous text is pink.

Phrases and Terms Pane

The Phrases and Terms pane contains all the components necessary for creating phrase structures in your data lens as follows:

New Phrase and New Term

These two icons can be dragged into the Graphical Rule Builder pane to create a new phrase or terminology node respectively.

Data Lens Folder

This folder is the parent folder for your entire data lens and contains all structures associated with the data lens. The nodes contained in these folders can be dragged into the Graphical Rule Builder pane to construct phrase structures.

Phrase Structure Folder

Contains phrase rules in use.

Terminology Folder

Contains terminology rules in use.

Common Folder

Contains commonly used phrase structures and terminology nodes.

Silver_Creek_Foundations Folder

Contains sample structures previously delivered in early versions of Enterprise DQ for Product. This is an older version of the Smart Glossaries Folder that may still exist if an importable lens uses this domain name. It does not exist when a Foundation data lens is not present.

Smart_Glossaries Folder

Contains all Smart Glossaries imported into the data lens; does not exist when Smart Glossaries have not been imported.

The following context-sensitive menu operates in both the Phrases and Terms pane and the Graphical Rule Builder pane:

Referenced By…
Displays information about the rule (phrase or terminology) including the Item Definitions, rules, and classifications that reference the selected rule.

Jump to Standardization

Activates the Standardize Phrases or Standardize Terms sub-tab on the Standardize tab with your selection so that you can edit the Item Definition.

Review Productions

Displays a dialog that allows you to review, move, or delete the productions that are associated with the selected rule or term. See "Modifying Phrase Productions".

Filter Data on rule name
Filters the sample data based on the selected rule and updates the Sample Data Table pane. The Unfilter button can be used to return the data to an unfiltered state.

Merge Rule…
Displays a dialog that allows you to merge the selected rule with another rule. See "Merging Two Phrase Rules".

Rename Rule
Displays a dialog that allows you to rename the selected phrase or term rule. See "Renaming Phrase Rules".

Delete Rule
Displays a verification message prompting you to review the consequences of the selected rule deletion including all the associated rules and productions that will also be deleted.

Edit Rule
Displays a dialog that allows you to edit the selected rule. See "Editing Phrase Rules".

Create New Parent
Creates a new parent term. See "Creating, Inserting, or Disconnecting a Parent Node".

Disconnect from Parent
Deletes the connection between a lower level node and a parent node.

Insert New Parent
Inserts a new parent term for the selected node. See "Creating, Inserting, or Disconnecting a Parent Node".

Attributes
See the following context-sensitive menu description.

The following Attribute Rule context-sensitive menu is slightly different for phrase structures and terminology nodes though the menu options work the same. Setting the various attribute options results in these changes to data lens processing:

Do Not Translate
The rule will not appear on the Translation tab and is not available for translation selection. See "Translating Data".

Format to Locale

The selection is automatically formatted based on target language requirements.

Translation Variable
Indicates that the rule will only be translated once and thereafter used as a variable that is reused during translation.

Prohibit Rename
Ensures that the rule cannot be renamed.

Prohibit Anchoring
Does not allow the rule to be anchored to any phrase.

Anchor
Anchors the rule to a phrase; this is the default.

Promote Children
Promotes all children of the rule to use the same value of the parent attribute.

Sample Data Table pane

The Sample Data Table pane contains the lines of sample data for the selected sample data file. The columns of the table, left to right, indicate the following:

Line Number (#)

The unique number assigned to that line of data.

Refresh

This column contains a refresh icon if the line of data must be refreshed; the Phrases tab is also marked with the refresh icon. Refresh applies the modification to all lines in the loaded sample data file. If there are rules in the grammar which have not been applied to a particular line of data, this column will contain a refresh icon. Lines are refreshed one at a time by clicking on the line or refresh all of your sample data by clicking the Refresh button.

Ambiguity Count (AC)

Indicates the ambiguity count for the line of data and corresponds to the pink colorized text so that you can resolve them. See "Resolving Phrase Ambiguity".

Unparsed Count (UC)

Indicates the unparsed terms in the line and corresponds to the gold colorized text. This allows you to easily identify the term and phrase rules that you must create so that your data is parsed completely.

Number/Percentage of Lines Recognized

The column heading indicates the number of lines and percentage of data that is recognized by the data lens. It is updated when the data is refreshed and as rules are created or edited.

Each of the columns that contain data can be used to sort the table, both ascending and descending, by clicking on the column title. Clicking a column heading once sorts the table by the items in the selected column, in ascending alphabetically order. Clicking the same column heading a second time sorts the table again in descending alphabetical order.

The following context-sensitive menu operates in the Sample Data Table pane and the options result in the following actions:

Mark Lines for Delete
Changes the # column to red for all of the selected lines though the lines are not deleted.

Un-Delete Lines

Changes the lines that are marked for deletion back to a normal state.

Apply Deletes
Deletes all lines of data that are marked for deletion permanently. This option is active only when there are lines marked for deletion. Deleted lines cannot be retrieved so this option should be used carefully.

Append Sample Data

Appends additional sample data from the data entry line to the current data source file.

Item Definition Field

This field displays the Item Definition for the line selected in the Sample Data Table pane; it is for review only.

Graphical Rule Builder Pane

This pane is used to create visual representations of phrase and terminology structures that identify the text components in a line of data. To create or modify a rule for the selected line of data, you drag and drop the phrase or term rules from the Phrases and Terms pane into the Graphical Rule Builder pane.

The following context-sensitive menu operates in the Graphical Rule Builder pane:

Predict Terms
All possible rules that could apply to the input data for an individual sample row, based on confidence ratings and meeting the Prediction Threshold, are displayed for you to choose from or a message that advises you why no predictions are available. Predict Terms only works in the context of Item Definition where the sample row has an associated Item Definition. You can select the appropriate

rule or reject the predictions. Rejecting predictions is only applicable to the current data lens editing session and is reset when you close the data lens. This option is also on the Edit menu. See "Using Rule Predictions".
Predict Best

Displays the closest matching phrase rules for the unparsed text in the selected record, which are automatically selected, based on confidence ratings and meeting the Prediction Threshold, for you to choose from or a message that advises you why no predictions are available. Predict Best only works in the context of Item Definition where the sample row has an associated Item Definition. See "Using Rule Predictions".

Predict Required Attributes
Displays only the matching phrase rules for the unparsed text in the selected record that match Item Definition Required Attributes. See "Using Rule Predictions".

Predict Missing Attributes
Displays only the matching phrase rules for the unparsed text in the selected record based on the attribute order specified in the Item Definition. See "Using Rule Predictions".

Predict All
You can select this option to examine all unparsed data in all records and automatically accept all predictions See "Using Rule Predictions".

Undo Predict All
Reverses changes effected using the Predict All option; only available after Predict All has been selected. See "Using Rule Predictions".

Layout Nodes
Realigns and redisplays the nodes in the Graphical Rule Builder pane. This can be very useful when you made several modifications to a rule.

New Term
Adds a new term. See "Creating Terminology Rules for Unknown Items".

Change to Regex
Changes the selected node (term rule production) to a regular expression. This is most useful for numbers and symbols, or when there is text within a string that does not contain spaces. See Appendix A, "Regular Expressions."

Selected Line Field

The Selected Line field allows you to edit the selected line of data and add those changes to your sample data. The data in this field is the same data that is displayed in the Graphical Rule Builder pane. You can further separate the selected lined of sample data, to produce more granular phrase rules, in this field.

The following context-sensitive menu operates in the Selected Line field:

Add to Sample Data
Adds all of the edits that you have made in the Selected Line field to your sample data.

You can also use the Selected Line field to enter in trial content, or content not contained in your sample data file. For example, you could enter "New sample data" into this field, and then use the Add to Sample Data option. This adds the entered text as a new term as in the following figure:

Enter an ID or you can leave the field blank, and then click OK.

The new line appears as the last line of your sample data file.

Using These Tools

The following sections explain how to use these tools.

Creating Terminology Rules for Unknown Items

There are three ways to create terminology rules for unknown items as follows:

- Use the context-sensitive menu, by right-clicking on the green text node, then clicking New Term.

- Right-click on a blue [Unknown] node and click New Term. You will be prompted for the term name. Create a terminology rule for the text 'mechanical'.

- Double-click the green text node to create a terminology rule with the same name.
You should only use the double-click method when the text is an exact match for the name you want to use for the name of the terminology node because this also provides a default value for the full form for this term rule.

All of these methods result in appearance of the New Term Name dialog.

1. Enter a descriptive name for the new term rule.

2. Select the check boxes as appropriate:
Generate Term Variants

Automatically generates the term variants for the rule as you create terminology rules. For example, rsstr is an variant for RESISTOR.

Generate Plurals

Automatically generates the plurals of the rule as you create terminology rules to aid in recognizing variants of the full form term.

Make into a Regular Expression

Converts the productions of the term rule into a regular expression in the full form of the term. Typically, this is only useful for numbers or symbols.

3. Click OK to add the new term. The new terminology node appears in the Phrases and Terms pane.

As a best practice, you should have a full form included when you build term rules. This will enhance predictions. For more information about predictions, see "Using Rule Predictions"

Creating Phrase Structure for Terminology Nodes

This section describes how to create the higher level phrase rules that contain the term rules. A well-formed phrase structure has at least one term node and at least one phrase node.

You can create a new phrase structure by dragging the New Phrase icon at the top of the Phrases and Terms pane onto an existing phrase or terminology node, by right-clicking on a terminology node and clicking Create New Parent, or by double-clicking a terminology node. Build your new rule as previously described.

Combining Two Phrase Rules into One Phrase Rule

Combining two nodes together allows you to join them together as one semantic concept and consolidates the phrase knowledge in your data lens.

To combine text nodes to form one production, use the following steps:

1. Create a new phrase as a parent for a term node.
Since this is a generic measurement, it is a good idea to assign it a name that can be used for other parsing rules.

2. Select the term [millimeter], right-click and click Create New Parent.

3. Select [number] and create a phrase [a_size].

4. To connect the [a_size] phrase node to the [u_length] phrase node, click the lower level node [u_length] (it turns red).

5. Click in the center [a_size], and then click to drag yellow phrase ball to [u_length].

Lines defining a phrase structure now join the two nodes into a combined phrase structure.

Merging Two Phrase Rules

Merging rules is useful when you want to combine the productions of two rules into one rule.

To merge rules, use the following steps:

1. Right-click the term or phrase to be merged, and click Merge Rule….

2. Locate the term or phrase you want the selection to be merged into and press the Tab key to select that term or phrase.
Use the scroll bar to search for the term or phrase you want to merge into or to search for it, enter the then name in the Name field.

3. Click OK to merge the rules.

You can view the results of the merge rule operation by right-clicking the term or phrase; and clicking Review Productions.

Renaming Phrase Rules

You can easily change the name of a phrase rule by right-clicking the phrase in the Graphical Rule pane, and then select Rename Rule. Use the New Term Name dialog to rename this phrase rule. See "Creating Terminology Rules for Unknown Items".

Editing Phrase Rules

Right-click the rule you want to edit and select Edit Rule.

The Edit Rule dialog allows you to directly edit the selected rule and operates like a text editor. You use this dialog to examine the existing production for the correct syntax, edit as necessary, and then click OK.

Adding Variant Forms of Terminology

The most robust phrase and terminology rules include the variations that could be associated with a term node to ensure that sample data is parsed quickly and in a standard way.

For example, the term [medium] could be further defined to include occurrences of the term 'med' as a term variant.

To ensure variations of the term are generated, edit the term as previously described and select Generate Term Variants.

Modifying Phrase Productions

All the phrase productions that have been created for a rule are displayed.

Select a rule, right-click on it, and select Review Productions. The following shows three different ways that [a_point_type] will be recognized in the data.

You can delete a production by selecting it, right-clicking, and then clicking Delete Productions. The other context-sensitive options result in the following actions:

Sort Productions

Sorts the listed productions in ascending alphabetical order.

Go to Production

Activates the selection in the Standardize Phrases sub-tab of the Standardize tab.

View Example

Activates the example text for the selection in an editable field for you to edit.

Using Rule Predictions

An expedient method for adding phrases and terms to existing rules is to use the Knowledge Studio Prediction feature. The Prediction options examine the unparsed source text, in the selected record or all records, to determine the rule that the text is most closely matches based on confidence ratings.

The Prediction options are activated based on the Item Definitions within the data lens and are described as follows:

Predict Terms

All possible rules that could apply to the input data for an individual sample row, based on confidence ratings and meeting the Prediction Threshold, are displayed for you to choose from or a message that advises you why no predictions are available. Predict Terms only works in the context of Item Definition where the sample row has an associated Item Definition. You can select the appropriate rule or reject the predictions. Rejecting predictions is only applicable to the current data lens editing session and is reset when you close the data lens

Tip:
This option is also on the Edit menu

Predict Best

The closest matching phrase rules for the unparsed text in the selected record, which are automatically selected, based on confidence ratings and meeting the Prediction Threshold, are displayed for you to choose from or a message that advises you why no predictions are available. Predict Best only works in the context of Item Definition where the sample row has an associated Item Definition

Predict Required Attributes

Only the phrase rules for the unparsed text, in the selected record, that match Item Definition Required Attributes are displayed. Changes only affect the Required Attributes of the displayed Item Definition.

Predict Missing Attributes

Only the phrase rules for the unparsed text, in the selected record, that match based on the Order Attribute rule specified in the Item Definition are displayed. If the Item Definition does not have an Order Attribute rule specified, then all attributes are examined.

Predict All

All unparsed data in all records are examined then all predictions are automatically accepted and effected in the data lens immediately. The results are displayed in the Status Field.

Note:
No confirmation of this action occurs so if you want to reverse the effects on your data lens, you must use the Undo Predict All option.

Undo Predict All

You can select this option to reverse all changes effected by the Predict All option. Similar to Predict All, selecting Undo Predict All immediately executes a reversal of the changes and clears the prediction cache without a confirmation prompt. The results are displayed in the Status Field.

Note:
The prediction results are based on meeting the Prediction Threshold, which is set in the Prediction tab of the DataLens Options. Setting to 0% will yield the full set of available of predictions, but there may be false positives that result from such a low setting. If you use a very low setting in conjunction with Predict All, you may inadvertently add incorrect associations. You should review Predict All results carefully. For more information about configuring prediction options, see "Setting Data Lens Options".

These options are available only on the Define Phrases sub-tab only by selecting a record in the Sample Data pane, and then right-clicking in the Graphical Rule Builder pane.

The Select Rule Predictions dialog is displayed for all options when predictions are found with the exception of Predict All and Undo Predict All. The columns of data displayed are the original unparsed text, full form term variant that might match the text, matching term, matching phrase, matching attribute, and the overall confidence matching score by percentage.

When possible, the predictions with the highest match scores are selected for you to accept them quickly. You can select rule predictions to accept or reject them. Rejecting predictions is only applicable to the current data lens editing session and is reset when you close the data lens.

Once you have accepted a prediction, the terminology rule is updated to add the prediction, which is appended with %ati% %review%. When reviewing the term production, the prediction is displayed with a pink as in the following example.

Indicating that you have reviewed rule prediction, using the Reviewed check box, removes the %review% appended to the rule and changes the production from a pink ball to a green ball.

Resolving Phrase Ambiguity

It is possible to create more than one phrase structure that recognizes a string of text (one or more text nodes) within your line of data. The competing phrase structures result in ambiguities.

The output order of parse results can not be guaranteed when ambiguities exist.

An Ambiguity Count greater than 1 means that there is more than one set of rules that recognize the line of data. When the Ambiguity Count is greater than 1, this column displays in pink and the terminology and the term and phrase rules will be white.

The Next / Previous toolbar buttons (left and right arrows) become active. You can use them to view the phrase structures that match the line of content.

It is possible that the ambiguity is not in the viewable line of data, so you must use the left-right scroll bars at the bottom of the task pane to view the entire line of data.

Resolve the ambiguities that occur by merging, combining, and/or deleting rules so that you have certainty about how your data will be parsed.

After you have resolved the ambiguity, the ambiguity count returns to a value of 1 and there is no longer pink highlighting of the ambiguous term in your Sample Data Table pane. In addition, the Next / Previous buttons are no longer active.

Editing the Term Full Form

The Edit Full Form option allows you to modify the full form of the selected term, as well as, generate term variants and plurals as previously described.

In addition to editing the full forms for each individual rule, you can view and edit the full forms for all term rules in your datalens. See "Editing Multiple Phrases and Terms".

Creating, Inserting, or Disconnecting a Parent Node

You can add a new parent or insert a new parent node between existing nodes with these features. Simply select a node, right-click and select the appropriate function.

When adding or inserting a new parent, the New Phrase Name dialog appears and is used as previously described.

After disconnecting a node from its parent, the line connecting the two nodes disappears from the Graphical Rule Builder pane. However, the relationship between these two rules still remains in your data lens. You may need to edit the rules if you want to remove the child as a production from the parent.

Define Items Sub-Tab

Item Definitions are based on attributes, their values, and the relationships among Item Definitions within a lens. Data lens Item Definitions permit you to define an object in terms of its attributes. Item Definitions may be based on a hierarchical schema or taxonomy, either explicit or inferred. Item Definitions provide a highest level of the domain approach to defining products and their respective attributes.

Item Definitions Pane

The Item Definitions pane contains all of the items that have been defined in the data lens. It is a folder, hierarchical structure showing the product taxonomy top-level Item Definitions and all subordinate Item Definitions.

The following context-sensitive menus are available in this pane:

Expand Node
Shows the entire hierarchical structure for the selected node.

Create Top-Level Item Definition

Creates a top-level definition as described in this section.

Create Sub-Item Definition

Creates a top-level definition as described in this section.

Edit Item Definition

Activates the Item Definition Attribute pane so that you can edit the selected node. See "Item Definition List Pane".

Edit Item Definition Alias or Description
Allows you to edit the alias or description as described in this section.

Copy Item Definition
Copies the selected Item Definition to a new Item Definition, at the same level, using a name that you supply.

Rename Item Definition
Changes the name of the selected Item Definition to the one that you supply.

Filter Data
Filters the sample data in the Sample Data Table pane based on the selection.

Set Inactive for Production
Allows you to set the selected Item Definition to be inactive. See "Active vs. Inactive Item Definitions".

Activate this and all children for Production
Allows you to set the selected Item Definition and all of its children Item Definitions to be active for use in production. See "Active vs. Inactive Item Definitions".

Set Inactive this and all children for Production
Allows you to set the selected Item Definition to be inactive. See "Active vs. Inactive Item Definitions".

Export or Import Item Definition
Exports or imports an Item Definition into the data lens. See "Exporting and Importing Item Definitions".

Delete Item Definition
Displays a verification message prompting you to ensure that you want to delete the selected Item Definition.

Delete Item Definition and Children
Displays a verification message prompting you to ensure that you want to delete the selected Item Definition and all of its children Item Definitions.

Sample Data Table Pane

The columns are the similar to those in the Sample Data Table pane on the Define Phrases sub-tab (see "Define Phrases Sub-Tab") and operate the same way.

Item Definition Count (IC)

Indicates the number of Item Definitions recognized for the line of data.

Quality Index (QI)

Indicates the quality index for the line of data. This is a measure of the Attribute coverage based on the number of required and scoring attributes associated with the Item Definition.

The QI begins with 50 points for being in the Item Definition and 50 points for the total attribute coverage of required and scoring attributes, and then is computed as follows:

50 + 50 * (total number of required and scoring attributes/total number attributes)

For example, if you have ten total attributes with coverage of two required attributes and three scoring attributes, then the QI is 75 as follows:

50 + 50 (5/10) = 75 QI

A line of data having a QI of 100 has attribute values for each required and scoring attribute in the Item Definition.

Item Definition

Provides the name of the Item Definition for the line of data.

Item Definition Field and Selected Line Field

These fields are identical to those on the Define Phrases sub-tab (see "Define Phrases Sub-Tab".)

Graphical Rule Builder Pane

The Graphical Rule Builder pane operates identically to the same pane on the Define Phrases sub-tab (see "Define Phrases Sub-Tab".) However, the available context-sensitive menus in this Graphical Rule Builder pane that are different are as follows:

Referenced By
Displays information about the rule (phrase or terminology) including the Item Definitions, rules, and classifications that reference the selected rule.

Match Attributes
Displays the matching phrase structure in the line of data with the attribute in the Item Definition (for example, the {a_color} phrase is matched with the Color attribute.)

Reverse Ghosting
Works as a toggle to show the terms that are not associated with an Item Definition as inactive (transparent); this is the default behavior. If you select the option these terms will appear active, selecting it again resets the behavior to the default.

Using these Tools

The following sections explain how to use these tools.

Creating a New Top-Level Item Definition

Use the following steps to create a new Item Definition.

1. To create a new Item Definition, right-click the Item Definitions folder in the left pane, and click Create Top-Level Item Definition.

2. Enter a name for the Item Definition.

3. (Optional) Enter an alias (a label).

4. (Optional) Enter a description.

5. Click OK.
The new Item Definition is displayed in the Item Definition folder in the Item Definition pane on the left.

Creating a Sub-Item Definition

Creating a Sub-Item Definition uses the same process as described in the previous section though you must select the parent Item Definition first, and then select Create Sub-Item Definition.

Aliases

The following sections describe the use of aliases an Item Definitions and attributes.

Using Aliases to Label Item Definitions and Attributes

Aliases are convenient labels for Item Definition and attribute names. Use aliases, in particular, if you want downstream processing to reflect the label exactly (including spaces, capitalization, numerals, and special characters that are not available for Item Definition and attribute names).

Data lenses provide the input to other processes, and aliases help you match labels to standard labels used in the data.

Limitations on Alias Names

You can use almost any characters you want for aliases. For attributes, you can use the same alias name more than once per Item Definition. Additionally, wherever the attribute occurs in the Item Definition (it may be used more than once), it must have the same alias. You should maintain the same alias for an attribute throughout the data lens.

If you do not follow the naming requirements for aliases, error messages will occur or in cases where there are no other errors, the Item Definition or attribute may be created without an alias. In such cases, you can add an acceptable alias by editing the alias after you have created the Item Definition or attribute.

Editing Aliases

You can edit aliases by right-clicking the item labeled by the alias, selecting Edit Alias (for Attributes) or Edit Item Definition Alias or Description (for Item Definitions) and modifying the label (or any other information) in the dialog that is displayed. Item definition aliases and descriptions can only be edited from the hierarchy list, whereas you can edit attribute aliases from the list of attributes on the Item Definition Edit pane.

Aliases are not required. If you do not provide an alias, the attribute name is used.

Precedence Rules for Alias and Attribute Names that Differ

If the alias is not the same as the attribute name, the alias will overwrite the attribute name in the output. In the following example, the attribute Att_1_Name was given the alias of Pen and thus appears in column H of the spreadsheet as in the following figure:

Viewing Alias Information

You can view alias information when you create or edit the alias information. Additionally, you can also view alias information in 'tooltips' by hovering over the object as in the following example:

The Item Definition alias precedes the Item Definition description.

Attribute alias name information is displayed as in the following figure:

If the attribute also includes a description, that information follows the alias information. If only a description is provided for the attribute, only the description is visible when you hover your mouse over the attribute.

Aliases are also viewable with a mouse hover in the Item Definition List pane, as shown in the following figure:

Active vs. Inactive Item Definitions

The use of AutoBuild to create Item Definitions from your source data can be incomplete so if the Item Definitions are not closely reviewed during enhancement before the data lens is checked in for production use, the result is that a vast number of matches are found for the incomplete Item Definitions. This can also occur when you create an Item Definition and are in the process of populating it with phrases and term rules.

You can set the Item Definitions in your data lens to be included in the output data based on the active and inactive flags. The Knowledge Studio parses your data using all Item Definitions though does not standardize the output data in Production mode if the inactive flag is set. In other words, all Item Definitions are always used for recognition, but not always for output. This is applicable to Production and QA Oracle DataLens Servers only; Development servers ignore the inactive flag. By default, the Knowledge Studio operates the same as a Development server in that it ignores the inactive flag.

The Item Definition inactive flag can only be set from the Define Items sub-tab of the Phrases tab.

Setting the Inactive Flag

To avoid using an incomplete Item Definition when your data lens standardizes your output data, right-click the Item Definition then select one of the following options to set the inactive flag:

Set Inactive for Production

Only the selected Item Definition is set to inactive.

Set Inactive this and all children for Production

The selected Item Definition and all of its children are set to inactive.

The icon for the selected Item Definitions changes so that you can easily see which Item Definitions are set to inactive. Be sure to expand parent Item Definitions to see if children Item Definitions are also inactive.

To reactivate an Item Definition, including children if inactivated, select the Item Definition, right-click then select Activate this and all children for Production.

Viewing Inactive Item Definition Output Data

You can view the standardized output data of Item Definitions that you have set to inactive from the following tabs:

- Standardize Items tab, Test Attributes sub-tab

- Standardize Items tab, Test Item Standardization sub-tab

- Translate tab, Test Translated Attributes sub-tab

- Translate tab, Test Item Translation sub-tab

On any of these sub-tabs, from the View menu, select View as Production. The Item Definition output data that is set to inactive is displayed and marked 'Inactive in Production' as in the following example:

Note:
If your inactivated Item Definition participates in an Item Definition ambiguity, eliminate the ambiguity before proceeding.

Item Definition List Pane

This pane is activated by double-clicking on an Item Definition or by selecting one and using the context-sensitive Edit Item Definition menu option. The icons and labels that appear in the Item Definition List pane are the building blocks of the Item Definition. You can set scoring attributes for the selected Item Definition in this pane.

The following Item Definition terms are used:

Attribute

An attribute is a characteristic of an item. For example, point size, barrel color, and ink colors are all attributes of pens. Attribute values are things such as 'Fine point', 'black ink, and 'red ink'.

Attribute Importance

Not all attributes play the same role in helping to identify an object. For example the object name, 'pen', might be required for defining an object correctly though a barrel color may not be required. In Item Definitions, there are four ways to define attribute importance:

- Required - the attribute must appear for the object to be defined.

- Scoring - the attribute is not required; rather, it assists in defining the item through a scoring algorithm.

- Optional - the attribute is neither required for the defining of the item nor does it participate in the scoring algorithm. Optional attributes do not disambiguate results. When a term is used in scoring or required attributes in Item Definitions, it generally should be used in scoring or required attributes in all Item Definitions in the same data lens.

- Disallowed - must not appear for the item to be defined.

- Unused - the attribute is not used. It can be used as a temporary storage area for attributes if you are not sure if or how you want to use them in the Item Definition and is more useful when your Item Definitions are more complex.

Return to Item Definition List
Returns to the Item Definitions Pane.

Drag to Create New…

Use these options to set attribute importance.

Selected Item Definition

Displays the Item Definition and any attributes that are set and includes the attribute importance values.

Item Definition Score Table

This table, in the lower left hand corner of the pane, reports the Item Definition metrics the selected line of data. AM refers to the number of Required and Scoring attributes populated; Cnd refers to the number of Conditional attributes matched; Spn refers to the number of total characters spanned by the required and scoring attributes. If you have an line of data that results in an Item Definition ambiguity, reviewing the this table can help you to resolve it.

The following context-sensitive menus are available in the Item Definition List pane:

Add Attribute
Adds an attribute. See "Adding Attributes".

Append AnyOf Below

Adds a node (boolean operator) that appends any of the attributes below the selected node.

Append AllOf Below

Adds a node (boolean operator) that appends all of the attributes below the selected node.

Append OneOf Below
Adds a node (boolean operator) that appends only one of the attributes below the selected node.

Delete Contents
Deletes the selected attribute without verification when deleting one attribute. Ensure that you have selected the correct attribute before using this option since you will not be prompted to review your deletion.

When you are deleting any of the Append nodes, a deletion confirmation prompt appears to verify that you want to delete the Append node and all the nodes it contains.

Copy Contents
Copies the contents of an attribute or hierarchy to paste it into another. See "Copying and Pasting Attributes across Item Definitions".

Paste Replacement
Pastes the copied contents of an attribute or hierarchy to paste it into another. See "Copying and Pasting Attributes across Item Definitions".

The following context-sensitive menu is available when an attribute is selected.

Jump to Attribute Standardization
Active when you select an attribute or a production associated with an attributes; advances you to the Standardize Attributes sub-tab of the Standardize Items tab with your selection so that you can edit it.

Insert AnyOf Above

Inserts an AnyOf node above the selected node.

Insert AllOf Above

Inserts an AllOf node above the selected node.

Insert OneOf Above

Inserts a OneOf node above the selected node.

Rename
Changes the name of the selected attribute to the one that you supply.

Rename All
Changes the name of the selected attribute and all other attributes of the same name in the Item Definition to the one that you supply. It is applicable only to the active sub-tab.

Rename All Globally
Changes the name of the selected attribute and all other attributes of the same name in the entire data lens to the one that you supply.

Edit Attribute Description
Changes the name of the selected attribute description to the one that you supply.

Edit Alias
Changes the name of the selected attribute alias to the one that you supply.

The following context-sensitive menu is available when a phrase under an attribute is selected.

Edit Value Logic
Allows you to edit the attribute value logic. See "Changing the Attributes Value Logic".

Edit Search Logic
Allows you to edit the attribute search logic. See "Changing the Attributes Search Logic". This option is only available if you have selected the term or phrase rule for an attribute; it is not available if you select an attribute that the attribute contains.

Require Anchored Phrase

Requires that the attribute is anchored to a phrase rule; the selected phrase cannot have a parent.

Using these Tools

When you standardize using the Item Definition List pane, the changes only affect the selected Item Definition and any global standardizations are ignored for the selected phrase or term rule.

Defining Item Definition Attributes

Although the Scoring attributes are not required to correctly recognize the data, they do participate in the scoring of an item.

As a best practice, you should have a full form included when you build terms. This will enhance predictions. For more information about predictions, see "Using Rule Predictions".

Adding Attributes

An attribute can be added to any of the Item Definitions by right-clicking on a node and selecting Add Attribute.

Enter a name for the new attribute, (optionally you can add an alias and text to describe the new attribute), and then click OK.

Copying and Pasting Attributes across Item Definitions

Copying attributes from one Item Definition to other Item Definitions can help save time. This section describes how to copy an attribute from an Item Definition to other Item Definitions.

The following sample steps copy For_Sale_Packaging from the <Wooden_Pencils> Item Definition to the other Item Definitions in the data lens.

1. Open the <Wooden_Pencils> Item Definition.

2. Select For_Sale_Packaging in the Optional attribute.

3. Right-click it and select Copy Contents.

4. Click Return to Item Definition List.

5. Right-click on the top-level Item Definition, <Writing_Instruments> and click Paste For_Sale_Packaging from <Wooden_Pencils> to Hierarchy.

This pastes the attribute that you copied into the entire hierarchy below the top-level Item Definition.

Viewing any Item Definition shows that the For_Sale_Packaging node is part of the Item Definition. Attributes are copied with the same attribute importance that they are copied from.

Changing the Attributes Value Logic

It is possible that data is recognized erroneously by your data lens. For example, C and F are recognized as abbreviations for the temperature scales Celsius and Fahrenheit. However, C and F could also be term variants for other rules in the data lens. Value logic can be used to exclude invalid temperature values within the Item Definitions to rule out invalid temperature ranges.

Note:
When using value logic on numeric attribute values, your phrases must configured to use Unit Conversions, See "Creating a Unit Conversion for a Phrase".

To change the logic that is used to evaluate an attribute, right-click on the attribute and select Edit Value Logic.

The default values for the attribute are displayed. Depending on the attribute, the Type of Value section may not allow editing.

You can change the way the attribute value is evaluated using the Value Expression section. Only one of the evaluation methods can be selected as follows:

Comparison

The attribute is populated if the value associated with the production is within the defined comparison range.

Range

The attribute is populated if the value associated with the production is within the range entered.

List

The value is evaluated to see if it is in the list entered.

User-defined

An evaluation regular expression is entered.

None

No evaluation will be performed on this attribute.

Note:
When using value logic on numeric attribute values, your phrases must first configured for Unit Conversion. See "Deleting Unit Conversion Types".

Changing the Attributes Search Logic

To change the logic that is used to evaluate an attribute when searching, right-click on the attribute in the and select Edit Search Logic.

The default values for the attribute are displayed. Depending on the attribute, the Type of Value section may not allow editing.

You can change the way the attribute value is evaluated during searches using the Search Function section. Only one of the search methods can be selected as follows:

Sorted

The numeric values are sorted, and then the appropriate selection is made. The resulting value is the specified instance (for example, first, second, third, Nth, etc.) in the Sorted list.

Unsorted

The value is selected based on the order in which they occur in the line of data.

Any

All values are searched.

At start of line

The attribute is populated only if it occurs at the beginning of the line of data.

At end of line

The attribute is populated only if it occurs at the end of the line of data.

Unique in line

This feature is deprecated.

View Hierarchy Sub-Tab

You can review the relationship between terminology and phrase productions using the View Hierarchy sub-tab.

Top Down and Bottom Up Grammar Hierarchy Panes

These panes present phrase productions in a top down hierarchy on the left and the related terminology productions presented in the reverse hierarchy on the right. You can select a production in the either pane and when the highest level production in the opposite pane is reached the production expands as in the previous figure.

The context-sensitive menus for either the Top Down Grammar Hierarchy or Bottom Up Grammar Hierarchy panes have the following possible menu options:

Show Hierarchy From Here
Expands the entire hierarchy structure below the selected parent node.

Show Hierarchy in Other Tree
The selection is located in the opposite hierarchy tree and automatically expanded so that you can view the entire node.

Jump to Rule for Editing

Active only when you select a phrase, term, or production and activate the Define Phrases sub-tab with your selection so that you can edit it.

Regression Testing Sub-Tab

The purpose of regression testing is to validate that any maintenance to the data lens has not created ambiguities or issues compared to the phrases and terms that were previously defined. This is an important step that should be performed only when fine-tuning a mature data lens.

Regression sets are tied to the sample file they are created with. You should choose or create a sample file that covers a broad range of data so that you can best see what happens when changes are made. A good regression base is typically a large file, usually with more than a thousand lines in it.

Note:
The maximum number of lines for a sample data file is 5000 lines; ensure that regression base (whether it is 5000 lines or not) represents the full variability across your data set.

Regression testing is most useful in the Standardize Items, Classify, and Standardize tabs; the usefulness with the Phrases tab is limited.

Ensuring Regression Testing is Active

Open the data lens that you are working on and choose the sample data file that you want to run your regression test against.

To activate the Regression Test sub-tab on this and all tabs, from the Data Lens menu, click Data Lens Options. Click the Regression Testing Active check box and click OK.

The Regression Test sub-tab is a graphical rule representation of the regression testing results.

Before Pane

This pane contains a graphical representation of the phrase rules of the selected line of data in the Sample Data Table pane, as defined in the data lens before regression testing.

After Pane

This pane contains a graphical representation of the phrase rules of the selected line of data in the Sample Data Table pane and displays the differences for your review.

If there is no data displayed in the Before and After panes, the sample data has not been initialized; a regression base does not exist. For information about initializing the regression base, see "Creating and Updating the Regression Base".

Review Column

The red check mark or Review column indicates new or changed lines of data and the text on these lines should be reviewed. If the information in the Current Text column is correct and you want to accept the changes as valid progressions, select this check box so that the changes are incorporated in the regression testing.

Creating and Updating the Regression Base

The best practice in creating a regression base is to combine your sample data into a one file. Regression testing is most effective when performed on a mature data lens. See "Combining Sample Files".

Combining files does not remove any data; it simply combines the selected sample files into a new, larger file.

Next, make single changes to your regression base sample data file, check your regression sets, and update them as appropriate. Making multiple changes can make the regressions hard to read, which increases the chance that an error is overlooked or is much harder to fix.

To create the regression base, select the Create New Regression Base option on the File menu, and then select the sample data file that you want to use for regression testing. This initializes the regression base and displays the results in the After pane.

You can update the regression base with the reviewed and accepted lines of text (as previously described in Review Column) using the Update Regression Base option on the File menu.

Note:
You should only initialize or update the regression base if you have reviewed or accepted the sample data.

3 Standardize Data

The purpose of data standardization is to make your data consistent and clear. Consistent is ensuring that the output is reliable so that related data can be identified using common terminology and format. Clear is to ensure that the data can be easily understood by those who are not involved with the data maintenance process.

You can standardize data globally (throughout the data lens) or you can perform standardization at very detailed level. This chapter contains information about how to globally standardize data. For information about standardizing by Item Definition, see Chapter 4, "Standardizing Item Definitions."

Standardization controls how your output appears. It allows you to choose how you will output the terms, phrases, or specific attributes within your data set. For example, the data may contain several versions of the word 'highlighter'. Highlighter may appear in the data as 'hi-lighter' and 'hi-liters'. By standardizing the output of the highlighter rule, you can choose the standardization of this word to be 'highlighters', regardless of whether it appeared as 'hi-lighter' or 'hi-liters' in the input data.

Standardize Tab

The Standardize tab and the associated sub-tabs provide you with all of the functionality needed to globally standardize your data.

Standardize Terms Sub-Tab

The Standardize Terms sub-tab is the default when selecting the Standardize tab for the first time. It allows you to:

- assign a replacement method for all of the term variants that have been assigned to terminology rules,

- set case,

- and easily copy and paste replacement values.

Figure 3-1

Terminology Rules Pane

The Terminology Rules pane contains the entire set of term rules that occur in your lens. This includes the rules you created and any rules imported using the Smart Glossaries.

Double-clicking on a terminology rule or the adjacent plus (+) sign expands the rule and its term variants are displayed. These variants are also displayed in the Rewrite Rule pane.

Single-clicking does not expand the rule though the term variants are displayed Rewrite Rule pane.

Rewrite Rules Pane

The main area of this pane allows you to select one of four methods that your data lens will use to standardize terms by replacing the text expression as follows:

No Replacement

The variant is not changed when parsed; no standardization occurs to the term variants for the term rule.

Replace All

All variants are replaced with the text entered into the field. Any terms subsequently added to a terminology rule using the Define Phrases sub-tab are automatically standardized.

Regular Expression

All variants are replaced with the regular expression entered into the field.

Individual Replacements

Clicking in the Rewrite As column in the Replacement Table activates a field in which you can enter replacement text. Each variant can be changed individually to specify different text.

You can add more term variants by clicking the Add button. The new term variant can be added by clicking in the Original field of the new line, entering the given text, and then enter the replacement text in the Rewrite As field.

You can clear any Rewrite As field by selecting the Delete check box.

The context-sensitive menu for this pane can only be activated in the Replacement Table and the menu options are dependent on the replacement selection. The possible menu options are as follows:

Make this the Replace All value
Use this option to populate the value for the Replace All method.

Copy table to Clipboard

Use this option to copy the contents of the replacement table into your clipboard so that you can paste it into another application. For example, an Excel spreadsheet.
Paste Clipboard to table

Use this option if you have created text rewrite rules in another application and want to paste them directly into the replacement table rather then entering them individually.

Copy Full Form Button

You can quickly create rewrite rules based on the full forms defined for your term rules in your data lens using the Copy Full Form button. This button is active when the overall Terminology Rules folder, in the pane of the same name, is selected.

The default case for the Standardization Type currently in use is set in the Default Case section, and applied to the rewrite rules by clicking Copy Full Form.

For example, using the To Proper Case setting means that the full form of each terminology rule (non-abbreviated words) is the default output. The full form 'medium' is the output standardized form even when the abbreviation 'med' is in the original data.

Default Case Section

When your data is case-sensitive, you should standardize term variants by clicking the Set Case check box and then selecting how to change the case using the appropriate check box:

- TO UPPPERCASE

- to lowercase

- To Proper Case

- Keep Case

Each case type option is shown as the term variants will be standardized. The default is Keep Case, which leaves the case unchanged.

Standardize Phrases Sub-Tab

The Standardize Phrases sub-tab allows you to:

- Specify the order of the individual term and phrase elements within a phrase and how those elements are combined.

- Modify a phrase by adding a new terminology rule.

Nodes to Receive Rules Pane

The Nodes to receive rules pane contains a list all of the phrases in the data lens. It allows you to select nodes that you want to reorder so upon selection the node appears in the Ordering Rule pane.

When you make changes in the Ordering Rules pane, the changes to the phrase are reflected in this pane by a change in the phrase icon color or shape.

Sort Productions

The context-sensitive menu for this pane is activated once you have selected a phrase.

Sort Productions
Use this menu option to sort all of the productions for the selected node to view the changes that you have made using the Ordering Rule pane.

Nodes for Insertion Pane

The Nodes for Insertion pane contains a list of all of the terms in the data lens. If you want to standardize a production by adding an existing terminology rule, drag the nodes from the Nodes for Insertion pane to the Ordering Rule pane.

Ordering Rule Pane

The Ordering Rule pane allows you to manipulate phrases to create an exact ordering for each node in your data lens. It has two distinct functionality representations to provide you with the ability to change the rule order, join the production terms, or delete a production in this pane.

When you expand a phrase or phrase production in the Nodes to receive rules pane, the proper ordering functionality is displayed in the Ordering Rule pane.

Within the Ordering Rule pane, there are three operations you can perform on a production:

- add terms

- delete terms

- change the order of terms

Join Terms

The ability to join one term with another term gives you the capability to quickly standardize your data and ensure that it is concise. There may be certain phrases that it is helpful to join with a character other than a space. For example, you may want to remove the spaces surrounding the slash in '2 / Package' so that it appears as '2/Package'.

You can join terms, or concatenate, to affect a new rule for the productions of one node by selecting it to change the functionality in the Ordering Rule pane.

The three concatenation options as follows:

Join with Space

Each term in each production of this phrase is separated with a space in the standardized output.

Join with String

The terms in all multi-term productions associated with this phrase will be joined together by the string (character) that you specify.

Concatenate

The terms within each multi-term production of this phrase will be concatenated without a space.

Delete or Reorder Terms

When the active functionality is deleting or reordering terms, the original production order is displayed and productions that can be moved or deleted appear below it.

A term can be deleted in this pane by right-clicking on it and clicking Delete.

Phrase productions are dragged in the Reorganize Here section to create a new phrase standardization rule (reorder).

The Reset button returns the phrase to its original production and all changes are negated including deletions.

Note:
The Test Global Standardization sub-tab is used to the view all the global standardization changes made in the Ordering Rule pane.

Standardize Lines Sub-Tab

Use the Standardize Lines sub-tab to re-order line item descriptions based on the position of the phrase structure rules. This feature allows you to define a consistent order of the selected attributes within a description. It is used in conjunction with other Knowledge Studio standardization functionality.

Unit Conversion Sub-Tab

The Unit Conversion sub-tab task allows you to standardize units of measure that are used in your data.

A unit conversion type must be selected to activate this sub-tab. From the Data Lens menu, click Unit Conversion Types, and then select the appropriate conversion type.

Nodes to Receive Rules Pane

The Nodes to receive rules pane operates in the same manner as on the Standardize Phrases sub-tab (see "Standardize Phrases Sub-Tab"). Additionally, this pane on the Unit Conversion sub-tab indicates the phrase productions that are contained in the Unit Conversion Table.

Instructions Pane

The Instructions pane displays information directing you how to use the Unit Conversion Table Construction Wizard.

Unit Conversion Table Construction Wizard Pane

Using the Unit Conversion Table Construction Wizard, you can construct units of measure conversion tables to be applied against the phrase productions in your data lens.

All units of measure conversions are global and applied to all phrase productions in your data lens. In addition to selection a unit of measure, you can also select decimal rounding places for each production in a phrase rule.

Select the Fixed Rounding check box, and then use the arrows in the field below it to select the decimal rounding you want to apply globally, for the selected production of the selected phrase rule. This applies decimal rounding to all [number][u_length] productions associated with the phrase rule [a_size].

Source Data

Conversion Applied to Phrase Production

Mechanical Pencil 1.4999 cm Emerald Barrel

Pencil, Mechanical, 15.00 MM, Emerald Barrel

Mechanical Pencil .0001 m Emerald Barrel

Pencil, Mechanical, .10 MM, Emerald Barrel

Mechanical Pencil 1 in Emerald Barrel

Pencil, Mechanical, 25.40 MM, Emerald Barrel

You can apply unit of measurement conversions at the standardize attribute level, which overrides global changes. See the "Value Replace Table".

Test Global Standardization Sub-Tab

The Test Global Standardization sub-tab allows you to review the global standardization rules applied to your sample data.

Sample Data Table

This table displays the original data and the same data after it has been standardized. The five columns, left to right, indicate the following:

Line Number (#)

The unique number assigned to that line of data.

Quality Index (QI)

A number between 0 and 100 that represents the degree to which the line has been standardized.

Length (Len)

The number of characters that are in that line of data.

Red Check Mark

Data you have reviewed and marked as such by selecting the check box in that line of data.

Initial Text

The original, unstandardized line of data that was parsed by the data lens.

Standardized Text

The standardized form of the original line of data.

Each of the columns that contain data can be used to sort the table, both ascending and descending, by clicking on the column heading. Clicking a column heading once sorts the table, by the items in the selected column, in ascending alphabetically order. Clicking the same column heading a second time sorts the table again in descending alphabetical order.

Sub-Tab Data Fields

Selecting one of the lines in the Sample Data Table displays the following information in the data fields below the table:

Standardized Text Field

The standardized version of the original data.

Tagged Standardized Text Field

The fully tagged and standardized version of the initial data.

Formatted Text Field

The result of applying source formatting rules.

Source Text Field

The original data. This field can be edited and when you press Enter, you can review the immediate effects of the data lens.

Regression Test Sub-Tab

Regression testing is an important part of data standardization so that you can be sure that your data output is as you expect.

If the tab is not active, set the Regression Testing Active Data Lens option. See "Setting Data Lens Options".

There are two regression testing panes, the 'before' and 'after' states of your sample data.

Before Pane

This pane contains the data that has been standardized based on the rules defined in the data lens before regression testing. The text that appears on the selected line of data in the pane is also displayed in the Current Text field.

After Pane

This pane contains the text that has been standardized based on the rules defined in the data lens. The text that appears on the selected line of data in the pane is also displayed in the Regression Text field.

If there is no data displayed in the Before and After panes, the sample data has not been initialized; a regression base does not exist. For information about initializing the regression base, see "Creating and Updating the Regression Base".

In either the Before or After pane, the colorized text indicates the following:

RED

The data that has been removed.

GREEN

That the data has been added. All text should be reviewed for any issues and a visual comparison made between the left hand and right hand panes.

ORANGE

That the standardization has been applied to this term and both the regression and current data will be colorized.

Review Column

The red check mark or Review column indicates new or changed lines of data and the text on these lines should be reviewed. If the information in the Current Text column is correct and you want to accept the changes as valid progressions, select this check box so that the data is included in the regression testing.

Creating and Updating the Regression Base

The best practice in creating a regression base is to combine your sample data into a one file. See "Combining Sample Files".

Combining files does not remove any data; it simply combines the selected sample files into a new, larger file.

Next, make single changes to your regression base sample data file, check your regression sets, and update them as appropriate. Making multiple changes can make the regressions hard to read, which increases the chance that an error is overlooked or is much harder to fix.

To create the regression base, select the Create New Regression Base option on the File menu, and then select the sample data file that you want to use for regression testing. This initializes the regression base and displays the results in the After pane.

You can update the regression base with the reviewed and accepted lines of text (as previously described in Review Column) using the Update Regression Base option on the File menu.

Note:
You should only initialize or update the regression base if you have reviewed or accepted the sample data.

Standardization Types

Enterprise DQ for Product supplies a default standardization type. This allows you to add standardization rules immediately without having to add a standardization type.

Creating a Standardization Type

You can create your own standardization schemas to be used throughout your data lens. Standardization types are used with all standardization tabs and sub-tabs.

1. From the Data Lens menu, select Standardization Types….

2. Click the Add New button.

3. Enter the requested information to create your new standardization type that will be added as a selection option to the Standardization Types list.

4. If you already have a standardization type created and you want to reuse that knowledge in a new version of the same standardization, select the Base classification on other type check box, and then select the appropriate classification type from the Based On: list.

Note:
This check box is not active if there are no other standardization types.

5. Click OK.

You can select the new standardization type for editing using the standardization type list on the toolbar. When you select a standardization type from the toolbar list, the standardization rules associated with the selected type appear on the Standardize tab. As you maintain your phrases and terms on the Define Phrases sub-tab of the Phrases tab, all standardization types are updated. When you save your data lens, all changes to standardization types are saved.

Deleting Standardization Types

You can delete standardization types if necessary.

1. Ensure that you have checked in your latest data lens version.

2. From the Data Lens menu, select Standardization Types….

3. Select the standardization type that you want to delete, and then right-click on it.

4. Click Delete Standardization.
A deletion verification dialog is displayed.

5. If you want to delete the selected standardization type, click OK otherwise click Cancel.

6. Click OK.

For more information about the use of Standardization Types, see "Unit of Measure Standardization Types".

4 Standardizing Item Definitions

Standardizing individual Item Definitions is different than assigning global standardizations as described in previous chapter. With Item Definition standardization, you select how you want the data to be standardized within the context of a specific Item Definition and the standardization only applies to that one Item Definition.

For example, you might want to standardize pen color differently from pencil color. By creating a rule for the color attribute of pens, you can ensure that the output data contains colors as they relate to pens only. A separate color rule for pencils could be defined so that the output relates only to pencil colors. These rules would not globally standardize colors for all Item Definitions, only for the pen and pencil Item Definitions; these rules would override any global standardization.

Standardize Items Tab

The Standardize Item tab and the associated sub-tabs provide you with all of the functionality needed to standardize your data at Item Definition level.

Standardize Attributes Sub-Tab

The Standardize Attributes sub-tab is the default when selecting the Standardize Items tab for the first time.

The Standardize Attributes sub-tab has several distinct functional representations to provide you with the ability to numerically rewrite rules, automatically rewrite rules globally, or reorder phrase productions.

Item Definitions Pane

All of the Item Definitions in your data lens are displayed in the Item Definitions pane. You can select an Item Definition, attribute, phrase, or term.

Double-click on an Item Definition to expand the full attribute structure for viewing and standardizing. Alternatively, you can use the Expand Node option. However, if the parent Item Definition contains attributes (identified by a red, square icon rather than blue), this Item Definition and its children Item Definitions are automatically expanded and fully visible.

Note:
The attributes of a parent Item Definitions that only contains attributes (denoted with a red, square icon) can be standardized in the same manner as all other attributes.

Once you have expanded an Item Definition, the attributes are displayed and will remain so because they have been added to the Item Definition hierarchy and are now in memory. Displaying an entire large Item Definition hierarchy requires a great deal of memory so this behavior ensures that the Item Definition pane is populated quickly and with a minimum of memory.

The behavior of the Standardize Attributes sub-tab is dependent on the selection made in this pane. For example, the selection of a term results in the appearance of the Nodes for Insertion pane and the Rewrite Rules pane changes to an Ordering Rule pane.

The context-sensitive menu for this pane has the following possible menu options:

Expand Node
Use this option to expand the hierarchical structure of the current selection.

Copy to…

See "Sharing Item Definitions Standardizations".

Copy with Children to…
See "Sharing Item Definitions Standardizations".

Copy to another Item Definition…

See "Sharing Item Definitions Standardizations".

Jump to Standardization
Use this option to go to the Standardize Phrases sub-tab of the Standardize tab to set a global standardization rule.

Rewrite Rules Pane

The Rewrite Rules pane makes it easy for you to create standardization rules by providing four different methods that are based on the type of selection made in the Item Definitions pane.

Numeric Rewrite Rules

The Numeric Rewrite Rules representation of the Rewrite Rules pane allows you to standardize number-based Item Definition attributes based on numerical values, such as standard units of measurement.

The default Numeric Rewrite Rules pane is a text replacement.

The Unit of Measure for Table list allows you to select the numerical value you want to standardize in the table below it. This list is populated with all of the units of measure in your data lens.

Buttons

The buttons are used as follows:

Add Row

Adds rows to the table one row at a time.

Remove Row

Deletes the row that is active or the bottom row. Some rows cannot be deleted.

Caution:
There is no delete verification prompt and rows cannot be restored.

Sort Table

The table is sorted.

Options

The Text Replace and Value for Target options allow you to choose a method of replacement for the selected rules, by text or by value.

Text Replace Table

The columns of the text replace table operate as follows:

>=

Indicates the lower bound and cannot be edited.

Number

The number to replace with standardized text, which is automatically populated based on the entries in the Upper Bound column.

Upper Bound

A number at which you want this replacement rule to stop (upper bound) for the given Number. Depending on the preceding row, the fields in this column are either automatically populated or you can enter new data.

Replacement Text

Allows you to enter the text with which the specified Number will be replaced.

Value Replace Table

When you select an attribute rule (for example, [a_size]) you are able to edit it in the value replacement table.

The first three columns operate as previously described. The Target and UOM Spelling columns replace the Replacement Text column so that you can provide values rather than text.

The Target column is a list of unit of measurements from which you can choose.

The UOM Spelling column allows you to enter what spelling the output data will be for the selected Target.

If you want to delete a value replacement table associated with a particular phrase, right-click on the phrase and click Reset Table.

Simple Rewrite Rules

This simplified Rewrite Rules pane allows you to define new text for the selected rule by entering the replacement text into the Rewrite As field.

Automatic Rewrite Rules

Selecting a term from the Item Definitions pane, changes the Numeric Rewrite Rule pane to an automatic Rewrite Rules pane to allow you to set a global standardization rule for the selected term. This functionality is identical to the Standardize Phrases sub-tab of the Standardize tab and is described in the previous chapter.

Reorder Phrase Productions

Selecting a phrase production from the Item Definitions pane, changes the Numeric Rewrite Rules pane to an Ordering Rule pane to allow you to reorder the phrase or add productions. This functionality is identical to the Standardize Phrases sub-tab of the Standardize tab and is described in the previous chapter.

Order Attributes Sub-Tab

The Order Attributes sub-tab allows you to define the order in which Item Definition attributes are ordered in the output data.

Item Definition Pane

This pane allows you to choose only a specific Item Definition or all Item Definitions in your data lens.

Note:
The active selections in this section are based on which Item Definition you have selected.

Selection Pane

The Selection pane allows you to select which Item Definition attributes you want to match on (show) by moving attributes from the Ordered Item Definition Attributes to Show list to the Item Definition Attributes not Shown list. All of the attributes in the Ordered Item Definition Attributes to Show list will be used for matching and shown in your standardized output.

Attributes are moved between lists using the right and left arrows or by double-clicking on an attribute. The up and down arrows are used to change the match and output representation order.

The Reset All Rules button is only active when the Item Definitions folder has been selected. It can be used to reset all Null and Multiple Instance Handling settings for all of the Item Definitions in your data lens.

Null Handling

The Null Handling section allows you to choose how you want null attribute values processed and is applicable to all the attributes listed in the Selection pane. The processing choices for null values are as follows:

Allow Null

Empty attributes are allowed.

Replace Null

Empty attributes are replaced with the text entered in the field.

Ignore Null values

Empty values are ignored.

Multiple Instance Handling

The Multiple Instance Handling section allows you to choose how you want multiple attribute values to be processed. The processing choices are as follows:

Allow Multiple Instances

Multiple instances of an attribute are allowed.

Replace Multiple With

Multiple instances of an attribute are replaced with the text entered in the field.

Ignore Extra Instances

The first occurrence of an attribute is selected for output; all subsequent attribute values are ignored.

Concatenate Ordered

The unique multiple instances of an attribute are concatenated together with the specified string. Additionally, this operation sorts the attribute values (either numerically or alphabetically, depending on the nature of the attribute value.)

Numeric sorting is performed when all of the attribute values correspond to unit of measure values. If one or more of the attribute values does not have a unit conversion rule, then the numeric sort is not performed.

Note:
If a data lens from EDQP 5.6.2 or earlier contains concatenation rules designed to handle multiple attributes, it may be necessary to review your attribute handling selections and standardization rules in order to maintain the expected results.

Concatenate Unordered

The multiple instances of an attribute are concatenated together with the specified string. This operation neither removes the duplicate nor sorts the attribute values.

Match Weights Sub-Tab

The Match Weights sub-tab allows you to select and order the Item Definition attributes to be used for matching purposes. This sub-tab is not active until a Match Type is created as described in "Match Type".

Item Definition Pane

This pane allows you to choose one Item Definition for which you want to change the attribute match rankings.

Selection Pane

The Selection pane allows you to select, which Item Definition attributes you want to participate in matching. This selection occurs automatically by increasing or decreasing the number in the Number of required attributes list. The selection begins with the first item in the list and sequentially selects downward.

The up and down arrows are used to change the match ranking of the selected attributes, moving those that are most important to use for matching to the top of the list. These arrows are activated when you select one attribute from the list.

After you have identified your matching attributes in each Item Definition, your Match Type is complete. The use of these attributes to create matched items occurs within the Matching DSA.

Note:
Matching data is a complex process that is configured using the Application Studio, Knowledge Studio, as well as the Governance Studio.

Duplicate Matching

Duplicate and nearly duplicate data is identified and matched for an Item Definition. There are many reasons for wanting to identify duplicate data, both within and between data sets. The problem with duplicate identification is that records may not have identical forms so they cannot be found through standard string comparison methods. For example, 'Ballpoint Pen Refill Med Pt Black Ink 2 / Pk' and 'Ballpoint Pen Refill Medi, Point black, ink 2 / Pack' might refer to the same item, even though 'Pack' is spelled differently. Thus, matching is performed on standardized data.

The actual matching of data occurs within a DSA though the foundation for creating matches is in the data lens. The matching function is based on a comparison of standardized attribute values. If the match-attribute values for multiple lines of data are equivalent, then the items are identified as matches.

Depending on the use case you develop, you may want to match on part number, manufacturer, and brand. In that case, you would select these three attributes to participate in the matching process.

Another use-case example is one that requires matching items based on form, fit, and function. The match could be defined based on length, width, height, color, and material.

It is important to realize the following:

- matching requires both a standardization type and a match type

- matching is done on attributes occurring within a given Item Definition

- matching requires certain settings for null and multiple attribute handling

Test Attributes Sub-Tab

The Test Attributes sub-tab allows you to review your attribute standardization rules by Item Definition that are applied to your sample data to validate your results.

Item Definition Section

All of the Item Definitions in your data lens are listed for your selection.

The Show Value and Show Text options can be used to change the way the data is viewed. Select Show Text to ensure that units of measure are displayed.

You can test the attributes for any of the Item Definitions in your data lens by selecting a different Item Definition from the list. All of the data on this tab is changed to display the date related to the new Item Definition selection.

By default, only the data for active Item Definitions is displayed. For information about viewing inactive Item Definition data, see "Active vs. Inactive Item Definitions".

Sample Data Table

This table displays the original data and the same data after it has been standardized. The columns, left to right, indicate the following:

Line Number (#)

The unique number assigned to that line of data.

Quality Index (QI)

A number between 0 and 100 that represents the degree to which the Item Definition for the line has been standardized.

Red Check Mark

Data you have reviewed and marked as such by selecting the check box in that line of data.

Initial Text

The original data that was parsed by the data lens.

Remaining Columns

The remaining columns are dependent on the attributes for each Item Definition so these columns vary.

Each of the columns that contain data can be used to sort the table, both ascending and descending, by clicking on the column title. Clicking a column heading once sorts the table, by the items in the selected column, in ascending alphabetically order. Clicking the same column heading a second time sorts the table again in descending alphabetical order.

Selecting one of the lines in the Sample Data Table displays the Item Definition information for the selection in the Standardized Attribute Table.

Source Field

This field contains the original data. This field can be edited and when you press Enter, you can review the immediate effects on the data lens.

Standardized Text Section

The standardized version of the original data is displayed in the field; it cannot be edited.

The Attribute Separator field allows you to specify a delimiter for the attribute values.

The Append Unattributed Text check box allows you to append the data that has been recognized though is not part of the Item Definition.

Similarly, the Append Unparsed Text check box appends unparsed text to your description.

Standardized Attributes Table

All of the standardized attributes for the selected Item Definition and the line selected in the Sample Data pane are displayed in this table. The attribute standardization selections from the Standardized Text section are reflected in the way this field is displayed.

Test Item Standardization Sub-Tab

The Test Item Standardization sub-tab allows you to review the Item Definition standardization rules that you have created applied to your sample data to validate your results.

By default, only the data of active Item Definitions is displayed. For information about viewing inactive Item Definition data, see "Active vs. Inactive Item Definitions".

Sample Data Table

Several of the columns of this table are the same as those on the Test Attributes sub-tab (see "Test Attributes Sub-Tab") and this table operates the same way. The differing columns are as follows:

Length (Len)

Indicates the character length of the original text.

Standardized Text

Indicates the standardized original text.

Source Field

This field contains the original data. This field can be edited and when you press Enter, you can review the immediate effects on the data lens.

Standardized Text Section

The standardized version of the original data is displayed in the field; it cannot be edited.

The Attribute Separator field allows you to enter a textual separator for use between attributes.

The Append Unattributed Text check box; selecting this box appends all text that has not been attributed to your description.

Similarly, the Append Unparsed Text check box appends unparsed text to your description.

Item Attributes Section

All of the attributes for the selected Item Definition are displayed in this field; it cannot be edited. The attribute standardization selections from the Standardized Text Section are reflected in the way this field is displayed.

Regression Test Sub-Tab

Regression testing is an important part of data standardization so that you can be sure that your data output is as you expect.

Standardize Items regression testing only works for lines that have an Item Definition. If there is no Item Definition that recognizes the line, no regressions are displayed.

If the tab is not active, set the Regression Testing Active data lens option. See "Ensuring Regression Testing is Active".

Sample Data Table

This table displays the regression and current view of the sample data and attributes.

If there is no data displayed in the Sample Data Table, the sample data has not been initialized; a regression base does not exist. For information about initializing the regression base, see "Creating and Updating the Regression Base".

The columns, left to right, indicate the following:

#

Line Number assigned to the line of sample data.

AC

Attribute Count displays the attribute count for the Regression and Current Attributes.

AD

Attribute Differences displays number of attribute differences between the Current and Regression files.

Regression

Item Definition assigned to the line of data based on the Regression file.

Red Check Mark

The red check mark or Review column indicates new or changed lines of data and the text on these lines should be reviewed. If the information in the Current Text column is correct and you want to accept the changes as valid progressions, select this check box so that the data is included in the regression testing.

The regression base is updated with the reviewed and accepted lines of text using the Update Regression Base option on the File menu.

Current

Item Definition assigned to the line of data based on the Current file.

ID

Unique identifier assigned to the sample data that was included as part of the sample data.

Standardized Data

This is based on any Global Line Order standardization that may have been setup.

Current and Regression Attributes Sections

These sections display the current and regression attributes for the selected line of data in the Sample Data Table.

Creating and Updating the Regression Base

The best practice in creating a regression base is to combine your sample data into a one file. See "Combining Sample Files".

Combining files does not remove any data; it simply combines the selected sample files into a new, larger file.

Next, make single changes to your regression base sample data file, check your regression sets, and update them as appropriate. Making multiple changes can make the regressions hard to read, which increases the chance that an error is overlooked or is much harder to fix.

To create the regression base, select the Create New Regression Base option on the File menu, and then select the sample data file that you want to use for regression testing. This initializes the regression base and displays the results in the After pane.

You can update the regression base with the reviewed and accepted lines of text (as previously described in red check mark column) using the Update Regression Base option on the File menu.

Note:
You should only initialize or update the regression base if you have reviewed or accepted the sample data.

Match Type

To use the Enterprise DQ for Product matching functionality you must create a match to be used throughout your data lens. You can create one or more match types that can be used to change how your data is matched depending on your use case.

Note:
You must configure how you want multiple instances and null values handled before you create a match type as described in"Order Attributes Sub-Tab".

This feature is accessed from the Data Lens menu, by clicking Match Types…, and then clicking the Add New button.

Enter the requested information to create your new match type that will be added as a selection option to the Match Types list.

The creation and selection of a Match Type is necessary to activate the Match Weights sub-tab.

5 Classify Data

The primary reason to classify data is to learn about it in a general way. You might want to find data without knowing a specific key or unique attributes or SKU that defines it. Often, you want to find data about a product in terms of the characteristics that define its properties or usage. For example, classifying parts data helps buyers find the parts they need to purchase. This is accomplished by defining or attaching the part to some type of classification system.

A classification system is typically a hierarchical structure where you can categorize the data for future retrieval. It may be broad covering a wide range of items, but having minimal granularity to differentiate similar items. Conversely, a classification system may consist of company-specific hierarchies that define the scope of the data.

Classification Schemas

The following sections describe the classification schemas that you can use in your Knowledge Studio data lens. Each data lens includes a sample set of e@Class, UNSPSC, and sample user-defined schemas.

UNSPSC

The Universal Standard Product and Services Code (UNSPSC) classification type was developed, and is maintained by the United Nations through the Electronic Commerce Code Management Association (ECCMA). The UNSPSC is a schema that classifies and identifies commodities. It is used in buy-side and sell-side catalogs and as a standardized account code in analyzing expenditure (Spend Analysis). It is a four-level classification system with provision for you to add a fifth level as needed. The Knowledge Studio is shipped with several versions of UNSPSC Classification Types for your use.

You can obtain the latest version from the UNSPSC Web site at

http://www.unspsc.org/

To use the downloaded zip file:

1. Extract the Excel file from the downloaded zip file.

2. Open the Excel File.

3. Delete the 'BTI' column

4. Save the file as a .csv file.
You can now uses this file to create a UNSPSC schema Classification Type. See "Classification Type"

The following is a sample from the UNSPSC classification schema, which is a hierarchical structure.

For more information about this classification system and the organization that supports it the UNSSPC Web site:

http://www.unspsc.org.

eCl@ss

Developed in Germany, eCl@ss has become the standard classification type for information exchange between suppliers and their customers. eCl@ss is characterized by a 4-level hierarchical classification system. eCl@ss maps market structure for industrial buyers and provides support for engineers during development, planning and maintenance. This is a schema that classifies and identifies commodities. It is used in sell side and buy side catalogs and as a standardized account code in analyzing expenditure (Spend Analysis). The Knowledge Studio is shipped with the latest available version of eCl@ss. For more information about this classification system and the organization that supports it, see the eCl@ss Web site:

http://www.eclass.de/

User-Defined

Enterprise DQ for Product provides file formats that allows you to build custom (user-defined) classification types. You can examine the sample user defined classification schema templates to decide which format will work best for you. For details, see "Creating User-Defined Classification Types".

Classification Type

In order to activate the Classify tab, you must first create a classification type.

Creating a Classification Type

1. From the Data Lens menu, select Classification Types….

2. Click the Add New button.

3. Enter the name for the Classification Type. This name is be added as a selection to the Classification Types list.

4. Select the type of schema to use to create your new classification type from the list.

5. If you already have a classification type created and you want to reuse that knowledge in a new version of the same classification, select the Base classification on other classifications check box, and then select the appropriate classification type from the Based On: list.

Note:
This check box is not active if there are no other classification types.

6. Click OK.
You are returned to the Classification Type dialog.

7. If you are creating a User-Defined classification type, you have the option to select either or both of these options:

- Global Classification File—Allows the update by a single user of the classification schema and these changes are made available to any data lens that is using that user-defined classification schema.

- Multi Classify—Allows a line of data to be classified to more than one category. The categories may be at different levels, but the classification rules must based on Item Definition, whether direct or indirect (for example, through the parent.) Any classification rule based only on terms and phrases is ignored.

8. Select a Master Classification File, and if applicable, a Master Synonym File using the Browse button.

9. Click OK to create the classification type.

Note:
If there is a problem creating the classification type it is likely that the schema did not load. Review the C:\Users\user_name\AppData\Roaming\DataLens\log\workbench.log file for problem details.

The first time you create a classification type, the Classify tab becomes active.

The selected classification type is displayed in the classification type list on the toolbar. When you select a classification type from this list, the classification rules associated with the selected type appear on the Classify tab ready for maintenance. As you maintain your phrases and terms on the Define Phrases sub-tab of the Phrases tab, all classification types are updated. When you save your project, all changes to classification types are saved.

Deleting Classification Types

You can delete classification types if necessary.

1. Ensure that you have checked in the latest version of your data lens.

2. From the Data Lens menu, select Classification Types….

3. Select the classification type that you want to delete, and then right-click on it.

4. Click Delete Classification Type.
A deletion verification dialog is displayed.

5. If you want to delete the selected classification type, click OK otherwise click Cancel.

6. Click OK.

If you delete the only classification type in your data lens, the Classify tab is rendered inactive.

Classify Tab

The Classify tab and the associated sub-tabs provide you with all of the functionality needed to classify your data.

Note:
You may want to set the Classification Context data lens option to ensure that parent-child classification relationships are maintained. See "Setting Data Lens Options".

Classify from Data Sub-Tab

The Classify from Data sub-tab is the primary mechanism for classifying content against specific data.

Classification Type Pane

The Classification Type pane displays the categories contained in the selected classification schema. A colored icon, the code number of the category, and the name of the category represent each category in the tree-type schema. The top-level category of the tree displays the name of the classification type, in this case 'UNSPSC_Writing_Inst'.

The use of Masks is described in "Advanced Classification Rule Functions".

Sample Data Pane

The Sample Data pane contains the lines of sample data for the selected sample data file. The columns of the table, left to right, indicate the following:

Line Number (#)

The unique number assigned to that line of data.

Classification Count (CC)

This column is zero when the line of data has not been classified. If the line has been classified to one category then this column displays a '1'. If the line has been classified to two different categories, then this column displays a '2', etc. This column is colorized in pink when the line of data has been classified to more than one category.

Category

When a line has been classified, the category number for the line is displayed. Double-clicking on the category identifier navigates to the specified category in the Classification Type pane.

Item Definition

The Item Definition for a line for which an Item Definition has been triggered. Not all records may have an Item Definition defined for it. In that case, there is not an Item Definition for a line of data in this column.

Number/Percentage of Lines Classified

The product description.

Standardized Text

The standardized form of the original line of data.

Each of the columns that contain data can be used to sort the table, both ascending and descending, by clicking on the column title. Clicking a column heading once sorts the table, by the items in the selected column, in ascending alphabetically order. Clicking the same column heading a second time sorts the table again in descending alphabetical order.

Item Definition Section

The field in this section displays the Item Definition for the line selected in the Sample Data pane. Double-clicking anywhere in the field automatically selects the Define Items sub-tab of the Phrases tab so that you can modify the selected Item Definition.

You can classify using the Item Definition by dragging the Item Definition icon to a category in the Classification Type pane.

Graphical Rule Pane

The Graphical Rule pane displays the phrase structure, for the selected line in the Sample Data pane, and allows you to see the full context and consequence of a classification action.

This pane is used as the source for linking the data with the classification system to create the classification transformation. To link data, you drag and drop the rule or Item Definition onto categories in the Classification Type pane, thus creating a classification rule.

Note:
The category node icons change from blue to red in the classification hierarchy of the item you just classified when you classify an Item Definition.

Classify from Item Definitions Sub-Tab

The Classification by Item Definitions sub-tab is the primary mechanism for classifying content against an external taxonomy or schema. If the Item Definition contains all the information required to classify a line of data, there is no need to use rules in the classification of data. However, if the Item Definition structure is less granular than the classification schema, then term and phrase rules may also be needed to perform the classification.

You can use multi-classification to classify from Item Definitions or Item Definitions + Rules. Multi-classification will not function if you drag only rules to multiple classification nodes. While you can classify rules to two different nodes on the classification hierarchy, only the first classification will output in, for example, a DSA or DGS project.

You can classify the Item Definitions in the right pane to a category in the Classification Type pane by dragging the Item Definition icon to the appropriate category on the left.

Item Definitions can be classified into any number of categories in the classification hierarchy using the same method. This is known as multiple-classification.

These Excel template files are installed on the user's system in the C:\Users\user_name\AppData\Roaming\DataLens\system\schemas directory.

Classify from Rules Sub-Tab

The Classify from Rules sub-tab allows you to link phrases and terms to categories.

By selecting phrases in the right pane and dragging and dropping them onto categories in the Classification Type pane on the left, you create a classification rule. The Classify from Data sub-tab displays the line of data you were classifying while this sub-tab displays the data lens phrases.

When classifying data you want to be as selective as possible. Since the UNSPSC classification type is a four level system, it is recommended that you classify Item Definitions to the fourth level whenever possible. However, classification systems are not always complete, so there are times when you may be forced to classify items to more general categories.

Test Classification Sub-Tab

The Test Classification sub-tab enables you to review the Classification of your sample data to validate your results:

Sample Data Pane

This table displays the original data and the same data after it has been classified. The columns are the same as in the Sample Data table on the Classify from Data sub-tab (see "Classify from Data Sub-Tab") with the exception of the last column on the right, which shows the standardized data.

Source Field

This field contains the original data. This field can be edited and when you press Enter, you can review the immediate effects on the data lens.

Standardized Text Field

The standardized version of the original data is displayed in the field; it cannot be edited.

Classification Pane

A subset of the Classification Type pane showing where the selected line item is classified.

Testing Multi-Classification

If the classification count is greater than one, the Next/Previous arrows on the toolbar are active. You can use the arrows to change which classification is visible in the Classification pane. The category name in the Sample Data pane does not change, however it does change in the test pane to display the next classification. The CC column, in the Sample Data pane, displays the multi-classification count.

Regression Test Sub-Tab

Regression testing is an important part of data standardization so that you can be sure that your data is classified as you expect.

If the tab is not active, set the Regression Testing Active data lens option. See "Ensuring Regression Testing is Active".

The Sample Data pane comprises this sub-tab and is the same as the same pane on the Test Classification sub-tab.

If there is no data displayed in the Sample Data Table, the sample data has not been initialized; a regression base does not exist. For information about initializing the regression base, see "Creating and Updating the Regression Base".

Creating and Updating the Regression Base

The best practice in creating a regression base is to combine your sample data into a one file. See "Combining Sample Files".

Combining files does not remove any data; it simply combines the selected sample files into a new, larger file.

Next, make single changes to your regression base sample data file, check your regression sets, and update them as appropriate. Making multiple changes can make the regressions hard to read, which increases the chance that an error is overlooked or is much harder to fix.

To create the regression base, select the Create New Regression Base option on the File menu, and then select the sample data file that you want to use for regression testing. This initializes the regression base and displays the results in the After pane.

You can update the regression base with the reviewed and accepted lines of text (as previously described in red check mark column) using the Update Regression Base option on the File menu.

Note:
You should only initialize or update the regression base if you have reviewed or accepted the sample data.

6 Translating Data

Enterprise DQ for Product is able to translate product data in batch or real time by leveraging the results of the data lens standardizations. Additionally, it is able to take a set of standardized attributes from one source language and store the corresponding target language translations in a translation glossary. This glossary file can be fully developed using the Knowledge Studio or can be exported using the export utility and sent to a translator for translation. The completed translation can be imported back in to the Knowledge Studio.

The translation glossary is used in real-time or batch to generate translated:

- Structured content of attributes based on the source data lens into one or more target languages.

- Descriptions into one or more target languages based on the translated attributes.

As in standardization, only the key attributes need to be parsed based on the data lens and the use case. Translation leverages all the work done in the data lens creation, recognition, and standardization phases of the project. The difference is the target language requirements.

Translation Process

Data Lens Standardizations Quality

The key to translation is to have a completed data lens with standardized attributes ready for the translation process. The translation is based on the currently selected unit conversion and standardization. You should carefully review and confirm the quality of all of the standardizations affected in your data lens prior to translation.

Prepare Data for Translation

Some text does not need translation. For example, codes, numbers, and proper names. The 'Do Not Translate' rule attribute leverages the semantic model interpretation of each attribute in context. This includes rules that deal with numbers, codes, proper names, etc. This attribute will flow through the data lens without requiring an entry in the translation glossary and therefore reduce the number of rules that must be translated.

Conversely, other text does need formatting to target local syntax. For example, numbers and currency. Identifying this text in the data lens reduces translations costs for units of measure where the unit of measure is translated only once, and Knowledge Studio automatically formats the numbers based on target language requirements.

Further, there is text that should be used as translation variables. For example, colors and materials. This reduces the translations costs for phrases that require different attribute ordering. The following example is for the [attr_color] phrase that is translated only once per distinct color. The [attr_color] phrase is then given the special translation attribute of 'Translation Variable'. This informs Knowledge Studio that any higher level attribute, which uses the term of [attr_color], will reuse the translation for the individual colors.

Translation Target

After you have the data lens completely standardized, you must identify translation targets to activate the Translation tab.

From the Data Lens menu, click Translation Targets.

The Select Target Locales dialog allows you to choose one or more languages that you want the data lens translated into from the Available Locales list.

The list on the left is the list all available locales; the list on the right is the list of all active locales. Use the arrow buttons between the two lists to move locales between the lists and complete your selections.

The Selected Locales populate the Translation Targets list on the toolbar.

You can choose as many translation targets as necessary so that the phrases and terms defined and standardized in a single data lens can be reused to define any number of translation results.

After selecting your translation targets, you can select any of them for editing using the Translation Targets list on the toolbar, which also changes the appearance of the Initial Translation button to the selected language icon. As you maintain your phrases and terms on the Phrases tab, and your standardizations on the Standardize tab, all translation targets are updated. When you save your project, all changes to translation targets are saved.

Translation Smart Glossary

By clicking on the Initial Translation button on the toolbar, you can import a translation Smart Glossary and apply that knowledge to accelerate the translation process. You select the appropriate Translation Glossary from the list.

Create/Update Oracle DataLens Server Translation Smart Glossary

You can either update an existing or create a new Oracle DataLens Server Translation Smart Glossary based on the completed translation in the data lens.

From the File menu, click Create/Update Glossary.

You select the appropriate action. To update, you must select the existing glossary from the list. To create a new translation glossary, you enter a descriptive name for the new glossary.

Note:
You must have selected at least one line using the Export check boxes to avoid an error.

Translation Tab

The Translation tab allows you to create translated data. There are three ways to generate translations for a translation glossary:

- Manually enter translated text into the sub-tabs of the Translation tab.

- Export all data for translation, and then import all translated data into the data lens.

- A combination of manually entering translated data and selecting specific data for export/import.

New Phrases and Known Phrases Sub-Tabs

You can create translations by entering the translation text directly into the New Phrases sub-tab. When you enter a translated phrase, select the red check box to indicate you want this translation included in the grammar, and apply it the fully translated phrase is added to the Known Phrases sub-tab.

These sub-tabs contain the same information and function in a similar fashion.

Translation Pane

Each contain the source language text in the Translation pane on the left and the translation text is on the right.

The data displayed in this pane can be changed toggling the Item Definitions and Parse Tree check boxes as follows:

Item Definitions

Displays Item Definitions in the list, as well as phrases.

Parse Tree

Searches the Item Definitions hierarchy for the selected text.

When the data lens is saved, the phrase translation change is written to the translation glossary in the locale directory of the knowledge base for the data lens.

Red Check Mark

The red check mark check box allows you to indicate the lines of translated text that you have reviewed on the New Phrases sub-tab, which are automatically selected on the Known Phrases sub-tab.

Export

The Export check box can be used on either of the sub-tabs to indicate the lines of text that will be exported into a list for external translation.

External translation can be performed by simply exporting only those phrases that require translation. This process saves time and money because attributes only need to be translated once. The data lens is capable of reusing attributes that have already been translated. The translated attributes can be imported into the Knowledge Studio.

Export Text for Translation

You export the phrases to be translated from the New Phrases sub-tab by selecting the Export check box for each of the phrases that you want to export. From the File menu, select Export Phrases for Translation.

This generates a file that contains the data lens name and a .trn extension in your export directory:

C:\Documents and Settings\Username\Applications\DataLens\data\export

or

C:\Users\Username\AppData\Roaming\DataLens\export

The exported translation file format is Unicode, a tab delimited format, and must be edited in a program that is Unicode compatible. Microsoft Excel is an example of a program that can save it as Unicode text.

The file has the following tab delimited format:

source

phrasetag

target

footed

[a_mounting]

Con Patas

rigid

[a_mounting]

Rigido

ring/stand

[a_mounting]

Anillo y Patas

Import Text for Translation

Once the phrases are translated, you can import them into your data lens.

The Known Phrases sub-tab must be selected as this feature is only active with this sub-tab. There are two import choices as follows:

Import Current Translated Phrases

Only the translated phrases that you have selected using the red check mark check boxes are imported.

Import All Translated Phrases.

All of the translated phrases contained in the import file are imported.

From the File menu, select Import Translated Phrases and the appropriate import choice. The default location is your export directory though you can select the exact location of the translated text file.

Once imported, the translations appear in the Known Phrases tab with the confirmed box checked, allowing a person to complete a final review for accuracy.

Source and Translated Text Fields

These fields contain the data source and translation text for that source for the selection in the Translation pane. The Source Text field cannot be edited. The Translated Text field can be edited and all changes are reflected in the Translation pane.

Export phrases and provide them to a language translator.

New and Known Variable Term Phrases Sub-Tabs

These sub-tabs operate like the Known Phrases sub-tab though the data that is displayed is different. The New Variable Term Phrases sub-tab displays all newly translated variables while the Known Variable Term Phrases sub-tab displays all of the translated variables that are known to the data lens.

Reorder Sub-Tab

A language translator, or other knowledgeable person, has the opportunity to complete a final attribute ordering using the Reorder Lines sub-tab to ensure that the translated phrase order is grammatically correct in the target language.

You can select a phrase from the left task pane to the Graphical Rule pane to form the reordered phrase that is appropriate for the target translation locale. When you have completed the reordered phrase, click Add to add it to the data lens.

Added reordered phrases can be cleared in the Graphical Rule pane or deleted by right-clicking on it in the right task pane and selecting Delete.

Use the translation testing sub-tabs to test your reordering modifications as described in the following section.

Test Translation Sub-Tabs

You can perform a final quality assurance check using the Test Translated Attributes, Test Item Translation, and Test Global Translation sub-tabs. Untranslated phrases are colorized blue while translated phrases are colored white (no color highlight).

By default, the data for both active and inactive Item Definitions is displayed. To view the data for active Item Definitions only (production data), from the View menu, select View as Production. For information about viewing inactive Item Definition data, see "Active vs. Inactive Item Definitions".

Test Translated Attributes Sub-Tab

The Test Translated Attributes sub-tab allows you to review the attributes for the translated phrases for an Item Definition to validate your results. This sub-tab is analogous to the Test Attributes sub-tab of the Standardize Items tab.

Item Definition Section

The Item Definition that was selected prior the selection of this sub-tab is displayed in the list. All of the Item Definitions in your data lens are listed for your selection.

The Show Value and Show Text options can be used to change the way the data is viewed.

Sample Data Table

This table displays the original data and the same data after it has been standardized. The columns, left to right, indicate the following:

Line Number (#)

The unique number assigned to that line of data.

Quality Index (QI)

A number between 0 and 100 that represents the degree to which the line has been standardized.

Red Check Mark

Data you have reviewed and marked as such by selecting the check box in that line of data.

Initial Text

The original data that was parsed by the data lens.

Remaining Columns

The remaining columns are dependent on the attributes for each Item Definition so these columns vary.

Each of the columns that contain data can be used to sort the table, both ascending and descending, by clicking on the column title. Clicking a column heading once sorts the table, by the items in the selected column, in ascending alphabetically order. Clicking the same column heading a second time sorts the table again in descending alphabetical order.

Selecting one of the lines in the Sample Data Table displays the Item Definition information for the selection in the Standardized Attribute Table.

Source Field

This field contains the original data. This field can be edited and when you press Enter, you can review the immediate effects on the data lens.

Translated Text Section

The translated version of the original text is displayed in the field; it cannot be edited.

The Attribute Separator field allows you to enter a textual separator for use between attributes.

The Append Unattributed Text check box; selecting this box appends all text that has not been attributed to your description.

Similarly, the Append Unparsed Text check box appends unparsed text to your description.

Standardized Attributes Table

All of the standardized attributes for the selected Item Definition and the line selected in the Sample Data pane are displayed in this table. The attribute standardization selections from the Translated Text Section are reflected in the way this field is displayed.

Test Item Translation Sub-Tab

The Test Item Translation sub-tab allows you to review the translated phrases for the Item Definition for your source language sample data to validate your results. This sub-tab is analogous to the Test Item Standardization sub-tab of the Standardize Items tab.

Sample Data Table

Several of the columns of this table are the same as those on the Test Translated Attributes sub-tab (see "Test Translated Attributes Sub-Tab") and this table operates the same way. The differing columns are as follows:

Length (Len)

Indicates the character length of the original text.

Standardized Text

Indicates the source language version of the standardized original text.

Translated Text

Indicates the translated version of the standardized original text.

Source Field

This field contains the source language text. This field can be edited and when you press Enter, you can review the immediate effects on the data lens.

Item Definition Field

All of the attributes for the selected Item Definition are displayed in this field; it cannot be edited. The attribute standardization selections from the Translated Text section are reflected in the way this field is displayed.

Test Global Translation Sub-Tab

The Test Global Translation sub-tab allows you to review the translated phrases for the Item Definition for your source language sample data to validate your results. This sub-tab is analogous to the Test Global Standardization sub-tab of the Standardize tab.

Sample Data Table

Several of the columns of this table are the same as those on the Test Translated Attributes sub-tab (see "Test Translated Attributes Sub-Tab") and this table operates the same way. The differing columns are as follows:

Length (Len)

Indicates the character length of the original text.

English, US

Indicates the source language version of the standardized original text.

Translated Language, Country

Indicates the translated version of the original text. The name of the translation language and country of origin are the column label.

Source Field

This field contains the source language text. This field can be edited and when you press Enter, you can review the immediate effects on the data lens.

Translated Field

This field contains the translated text and displays any changes made in the Source field.

Quality Metrics

The criteria for accurate translation are displayed for your review so that you can monitor the translation progress and include the following:

- the parse quality

- percent of text found in the Dictionary

- percent of overall quality indicators: Acceptable, Average, and Unacceptable

Regression Test Sub-Tab

Regression testing is an important part of text translation so that you can be sure that the translated is as you expect.

If the tab is not active, set the Regression Testing Active data lens option. See "Ensuring Regression Testing is Active".

There are two regression testing panes, the 'before' and 'after' states of your sample data.

Before Pane

This pane contains the data that has been translated based on the rules defined in the data lens before regression testing. The text that appears on the selected line of data in the pane is also displayed in the Current Text field.

After Pane

This pane contains the text that has been translated based on the rules defined in the data lens. The text that appears on the selected line of data in the pane is also displayed in the Regression Text field.

If there is no data displayed in the Before and After panes, the sample data has not been initialized; a regression base does not exist. For information about initializing the regression base, see "Creating and Updating the Regression Base".

In either the Before or After pane, the colorized text indicates the following:

RED

The text that has been removed.

GREEN

That the data has been added. All text should be reviewed for any issues and a visual comparison made between the left hand and right hand panes.

ORANGE

That the translation has been applied to this term and both the regression and current text will be colorized.

Review Column

The red check mark or Review column indicates new or changed lines of data and the text on these lines should be reviewed. If the information in the Current Text column is correct and you want to accept the changes as valid progressions, select this check box so that the data is included in the regression testing.

Creating and Updating the Regression Base

The best practice in creating a regression base is to combine your sample data into a one file. See "Creating and Updating the Regression Base".

Combining files does not remove any data; it simply combines the selected sample files into a new, larger file.

Next, make single changes to your regression base sample data file, check your regression sets, and update them as appropriate. Making multiple changes can make the regressions hard to read, which increases the chance that an error is overlooked or is much harder to fix.

To create the regression base, select the Create New Regression Base option on the File menu, and then select the sample data file that you want to use for regression testing. This initializes the regression base and displays the results in the After pane.

You can update the regression base with the reviewed and accepted lines of text (as previously described in Review Column) using the Update Regression Base option on the File menu.

Note:
You should only initialize or update the regression base if you have reviewed or accepted the sample data.

Translation Repair Formats

Translation repair allows full-line translations to refine following machine translation. The typical use of translation repair is to remove unwanted text that is inserted into the full line translations by machine translation.

For example, instead of 'la Red' you may just want 'Red', removing the definite article. It is possible that the 'la' appears in a number of places in the data that may not suit your purposes. If a problem like this is found repeatedly in your data, then you may need to use the Translation Repair Formatting feature. Alternatively, you could provide a phrase translation in the translation dictionary.

Keep the following in mind:

- The repair only works at the line level.

- The Translation Repair Formats menu item is enabled if you have purchased data lens translation functionality and installed on the Oracle DataLens Server.

To use this feature, from the Data Lens menu, select Translation Repair Formats.

This opens the Edit Translation Repair Formats dialog. This dialog lists all of the substitutions that are done following the translation of full lines. Each substitution appears on one line of the dialog. These substitutions are called format rules.

For example, the format rule 's/\sEL\s/ /gi' has four parts:

- Perform a search operation, denoted by s/.

- Identify text strings that contain a white space followed by 'EL' followed by another white space. Examples of white space include tabs and spaces, or a

- Replace the identified string with a space.

- The last forward slash followed by 'gi', are the substitution modifiers. The 'g' means perform the substitution globally within the line; without this modifier only the first instance is substituted. The 'i' means ignore the case. Thus, all of the forms of 'EL' including 'El' and 'el' will be substituted with a space.

Note:
For more information on pattern search and replacement, see Appendix A, "Regular Expressions."

It is important to test the results of the substitution; therefore, return to the Test Translation sub-tab and translate your sample content again. Filter your data to check that the intended translations have been made.

7 Generating Reports

This chapter describes the reporting functionality in the Knowledge Studio.

Function Specific Reports

These reports provide information regarding specific functionality within the Knowledge Studio.

Classification Reports

The classification reports can help you test your results and provide a record of important classification statistics.

From the Classify tab, select the Test Classification sub-tab. This report is only active from this location. From the File menu, click Report….

Select the report you want to generate and click OK.

The report is displayed in your default browser as in the following sample:

The classification report can be saved or printed from the browser.

Standardization Report

You can view the standardization reports relating to the results of standardizing the current sample data file with a Standardization Report.

From the Standardization tab, select the Test Global Standardization sub-tab. This report is only active from this location. From the File menu, click Report….

Select the Standardized Data List, and click OK.

The report is displayed in your default browser as in the following sample:

The standardization report can be saved or printed from the browser.

The Quality Index (QI) for each line in the preceding report is the percentage of the line that was recognized by the data lens. This number may closely correlate with the amount of standardized text, given that you have reviewed and standardized all of your terminology rules on the Standardize tab.

Grammar Reports

These reports provide information regarding the grammar of the rules defined within the Knowledge Studio.

From the Phrases tab, select the Define Phrases sub-tab. This report is only active from this location. From the File menu, click Report….

Select the report you want from the following:

Grammar Rules

This report shows all the rules; phrases first then terms with their respective productions.

Grammar Rules by Domain

This report shows all the rules with their productions, grouped by domain; phrases first then terms.

The report is displayed in your default browser as in the following sample:

The grammar report can be saved or printed from the browser.

Complexity Reports

This report shows the complexity of the sample data. From the File menu, click Complexity Reports….

The report is displayed in your default browser as in the following sample:

The complexity report can be saved or printed from the browser.

Semantic Reports

These reports create a text file that can be utilized with any tool to review the information provided. Semantic reports are based on a sample file that you can select at the time the report is generated. From the File menu, click Semantic Reports….

Select the report you want from the following:

Count Parsed Phrases

This text file counts the parsed phrase usage in the data lens for the selected sample file. This will show the Frequency of Phrase, Associated Phrase, and associated terms.

Count Parsed Phrases with Detail

This report counts the parsed phrase usage in the data lens for the selected sample file. It details the Frequency of Phrase, Associated Phrase, and associated terms.

Count Un-Parsed Phrases

This report counts the unparsed phrase usage in the data lens for the selected sample file.

Count Un-Parsed Phrases with Detail

This report counts the unparsed phrase usage in the data lens for the selected sample file.

Count Key Lines

This report counts the number of key lines in the project.

Count Translations

This report counts the number of translated lines in the project.

Select the sample file of interest. The report is generated and saved to a text file to a path that is displayed in the Status pane.

8 Managing Data Lenses and Files

This chapter explains the various tasks related to setting global data lens options, managing your data lenses and sample data files.

Data Lens Management

This section describes how to manage your data lenses.

Setting Data Lens Options

Various DataLens options that affect the operation of a data lens can be configured by you. From the Data Lens menu, select Data Lens Options.

The General tab is the default for setting data lens options. Select the options as follows:

Classification Context
Instructs Knowledge Studio to include classification parents with children on drag and drop functions within the classification schema.

Case Sensitive Text Parsing

Preserves case and is applicable only to term nodes (purple) after this option is set. Term nodes contain all variations of a particular text item. For example, 'Cap' and 'CAP' must be independently specified. Clearing this check box ignores differences in capitalization, 'cap' only needs to specified once. When this option is toggled, all terminology and phrase rules will be consistent with the new case sensitive mode.

Full Form / Generate Term Variants

Automatically generates term variant as you create

terminology rules. For example, 'RES' is an abbreviation for 'RESISTOR'.
Generate Phrase Variants

Results in the automatic creation of Terms and Phrase productions from standardized output.

Apply Button also Refreshes sample data display
Changes the Apply toolbar button so that it refreshes interface panes, as well as, applies editing changes to the currently open sample file.

Regression Testing Active
Activates the Regression Testing tabs throughout the Knowledge Studio.

View Hierarchy
Results in the full display of all hierarchical structures.

Importable Lens
Allows the Data lens to become available for import into other data lenses once it is checked into the Oracle DataLens Server. These data lenses are called Foundation Data Lenses.

Minimum Classification Level
Allows you to determine the lowest level of classification that can be defined within the data lens. Maximum level is 5.

The Caching tab can be selected to set the data lens cache parsed data results.

When selected, after each line of data is parsed the results are cached to the global cache so that it does not need to be re-parsed by that lens, which increases the speed at which data can be processed.

The Prediction tab can be selected to set the options associated with the terminology prediction functionality. If the data lens and associated DSA do not contain predictions, this tab is not active. Select the options as follows:

Prediction Threshold (%)
Allows you to set the phase rule confidence rating threshold from zero to 100 percent. This threshold is used to determine whether unrecognized input text matches an existing term thus becoming a prediction.

Note: Setting this option to zero may produce erroneous results so you should carefully examine any predictions before accepting them.

Ignore optional missing attributes

Allows you to specify that the prediction function ignore missing attributes.

Missing attributes to ignore:
Allows you to provide the attributes that you want to be ignored by the prediction function.

Refresh prediction fullform cache

Refreshes the cache of full-form term variants that is maintained for prediction function use when OK is selected. The option is reset after each use.

The Bidirectional Text tab is active when the Enable Bidirectional Text Dialog option is selected. See "Setting Knowledge Studio Options". You can select it to set the options associated with the bidirectional text functionality. Select the options as follows:

Enable bidirectional processing
Enables additional text pre-processing of input text for bidirectional languages necessary for correct parsing of visually contiguous text. These languages include Arabic and Hebrew.

Force bidirectional processing

Enables the additional text processing of any input text that contains bidirectional characters regardless of the specified source language. This can be helpful when the source contains some right-to-left text irrespective of the specified language.

Show all standardizations bidirectionally

Enables the forcing of conditional bidirectional display of standardized text for the data lens regardless of the specified source language.

Show all translations bidirectionally
Enables conditional bidirectional display of translated text for the data lens regardless of the target language. This is helpful when translating text from a right-to-left language into a left-to-right language and the translation is incomplete. Setting this option adjusts the orientation of the translated text in any target language based on its content and improves the display of the translated text.

Checking In a Data Lens

You can only check in data lenses that have not been previously checked in or those data lenses that you (the current user) have previously checked out. Checking in a data lens changes it to read-only.

1. From the Data Lens menu, click Check-in Data Lens….

2. Enter a check in comment, which will be associated with the new revision. You can use the Previous Comments button to view and copy the text of other comments for use in the new check-in comment.
Make selections from the check boxes that function as follows:

- Deploy to Development after Check-In
The data lens is available for use by others after check-in and that the version in use is updated for use by DSAs.

- Keep locked for more editing
The data lens cannot be check-out by other users for editing. Clearing this check box will make the data lens 'read-only' and activates the Delete local Data Lens check box.

- Delete local Data Lens
Deletes this data lens from your local drive.

3. Click OK.
A verification that the data lens has been closed is displayed and the data lens itself is closed.

Checking Out a Data Lens

You can check out only those data lens that you (the current user) have checked in.

1. From the Data Lens menu, click Check-Out Data Lens….

2. Select the data lens that you want to check out from the list of available data lens.
The Revision cannot be changed and increments automatically.

3. If you want to review the check in/out history of the selected data lens, click View History. The Revision History dialog appears listing all of the comments for each revision of the data lens.

4. Select whether you are simply checking out the selected data lens or that you want to base a new data lens on the selection using the two options. If you select Check-Out for NEW Data Lens, then you must provide a new name in the field provided.

5. If are checking out a data lens and do not want to edit it, select Lock server file for editing so that the data lens is read-only.

6. Click OK.
A verification that the data lens has been closed and is 'read only' is displayed.

Deleting Data Lenses

You can delete an existing Data lens that is on your local computer from within the Knowledge Studio.

1. Open the data lens you want to delete.

2. From the Data Lens menu, click Delete Data Lens.

3. A warning to verify that this action should be taken is displayed.

4. Click OK to delete or Cancel to keep the data lens.

Deleting Read-only Data Lenses

Additionally, you can delete any read-only (checked in) versions of data lens that reside on your local computer.

1. From the File menu, click Delete Read-Only Lenses.

2. Select All or the individual data lenses you want to remove.

3. Click OK to delete the selected data lenses.
The selected data lenses are deleted from your local drive.

Changing Data Lens Information

This section explains how to view and change the information associated with a data lens.

Editing a Data Lens Description

A description can be provided to the data lens that provides a detailed description of the lens.

From the Edit menu, click Edit Lens Description. Enter a description to be assigned to the data lens and click OK.

Editing Data Lens History Notes

This functionality provides an informational history about the lens; this can be used to track maintenance that has been performed.

1. From the Edit menu, click Edit Lens Description….

2. You can enter your own information to the existing list.

3. For each entry in the list, you can assign a timestamp by placing the cursor at the end of the entry and clicking Add Timestamp.

4. Any entry, including timestamps, can be modified.

5. Click OK to save the history notes.

Viewing Tasks

You can see if you have any tasks assigned to use with this feature.

From the View menu, select View My Tasks.

All assigned tasks are displayed in the top pane, while the bottom pane provides the details for the selected task.

Note:
Though the fields in the bottom pane appear to be editable, the changes are not saved.

The context-sensitive menu in the top pane is activated for each task by right-clicking anywhere in the task line and is used as follows:

Change Task Status
You can changes the status of the task, see "Changing the Task Status".

Download Attachments
You can download the file that was saved when the task was created for use in completing the task. If this is a data enrichment task, this is the file that you can import into your data lens. See "Importing from a Downloaded File". A file save dialog appears for you to select the directory in which to save the file. The default download directory is in the user's C:\Users\user_name\AppData\Roaming\DataLens\tmp directory.

View Enrichments

This option is displayed when there are data enrichment tasks available for review; these tasks are identified with "Enrichments for Data Lens" prepending the task description. See "Viewing Data Enrichment Tasks".

Create Tasks
Create a new task, see "Creating a Task".

Changing the Task Status

Selecting this option allows you to change the status of the task and/or reassign the task to another user.

1. Select a new status and/or a user to reassign the task to from the lists.

Tip:
You can use the Unassigned Tasks user if you are unsure who you want to review this task and intend to assign it the proper person later.

2. Enter a comment that reflects why you have effected the change for future reference or to alert the new recipient of the task why they are now responsible for it.

3. Click OK.

Viewing Data Enrichment Tasks

Selecting this option allows you to view all of the data enrichments that associated with the tasks that can be imported into your data lens are displayed in the upper pane. Additionally, any data enrichment suggestions submitted by user's for you to consider manually adding to your data lens are displayed in the lower pane.

There are no actions available in the View Enrichments dialog box as this is informational only. You can use it before attempting to import enrichments into your data lens and is particularly helpful when you are importing all of the enrichments in the file available for download from the View Tasks dialog box. See "Importing Enrichments".

Creating a Task

Selecting this option allows you to create an entirely new review task.

1. Select a user to complete this task.

2. Select the DSA and the DSA step that you want to change.

3. Select the data lens to which the change is to be applied.

4. Enter a description and specific instructions on how to perform the task.

5. If you have a data file or other information that you want to attach, click Add Attachment, locate the file, and then click OK.
Repeat this step until all necessary files are attached.

6. Click OK.
The task is created and an email containing the task details is sent to the assigned user.

Sample Files

This section explains how to work with data lens sample data files.

Creating New Sample Data

This feature allows you to create new sample data files to add to your existing set.

1. From the Edit menu, select New Sample Data.

2. Use the default numerical file name prefix or enter your naming convention.
The names are similar to the files created during new data lens creation and will include the specified prefix. For example using the default numerical prefix, the first time you import sample data, the files will have names such as 2-baseline.xml, and 2-sample1.xml. Similarly, the next time you import sample data, they will have names like 3-baseline.xml and 3-sample1.xml.

3. Select the appropriate type of character encoding from the list.

4. Select the Data Source type from the popup menu by clicking Select… and locate the file you want to use.

5. Enter the number of sample files and lines contained in each file.

6. Click OK.
New sample files are created in the data sub-directory of your data lens.

Deleting Sample Files

Use this option to delete sample files that were created and associated with a data lens.

1. From the File menu, click the Delete Sample Files….

2. Select all sample files or the specific sample files you want to delete.

3. Click OK to delete the selected sample files.

Renaming Sample Files

This can be used if a group of sample files needs to be renamed for better identification.

1. From the File menu, click the Rename Sample Files….

2. Select the sample file prefix to rename from the list.

3. Enter the new file prefix you want to use.

4. Click OK.
The sample file name changes can be reviewed from the File menu by clicking Select Data File… and reviewing the displayed list.

Combining Sample Files

This functionality can be used if a new regression sample file, needs to be created that is a combination of existing sample files. The original sample files are not modified or deleted.

1. From the File menu, click Combine sample data.

2. Select the sample files that should be combined and click OK.

3. Enter a name for the new sample file.

If you want to open the newly created file, from the File menu, click Select Data File…. Then locate and open the new file.

9 Export and Import Features

This chapter explains how to import and export data lenses, phrase and terminology rules, Item Definitions, and importing enriched data.

Exporting a Data Lens

A data lens can be exported, which may be useful for archival and back up purposes.

To export a data lens, on the File menu, click Export Data Lens. Enter a descriptive filename for the data lens, and then click OK.

All exported data lenses are stored in the export directory, …\Application%Data\export.

Importing a Data Lens

An exported data lens can be imported for use.

To import a data lens, on the File menu, click Import Data Lens. Locate and double-click on the data lens to start the import process.

The Knowledge Studio checks to see if this data lens exists locally and if it does exist, you are prompted to rename the data lens to avoid overwriting files.

Note:
You must still check in the data lens to the Oracle DataLens Server in order to use it in applications.

Exporting Rules

Lens rules can be exported to a text file; this output is useful for reviewing the rules and comparing the existing rule set to prior rule sets.

You can check out a version of the data lens, create a text file, and then check out a prior version of the same data lens. Next, using any tool that allows a comparison to be generated, you can compare the differences between the files. This functionality is used if maintenance has been performed to a data lens and the results were not as expected.

Rules can be exported for an entire data lens or by Domain.

The rules are exported to the default export directory, …\data lens name\Application Data\DataLens\export, or you can specify the path and file name.

Note:
Exporting data lens rules is not the opposite of import rules; exported rules are not in a format that can then be imported.

Importing Phrases and Terms

Knowledge that may exist within your organization can be used as part of the knowledge building of terms, phrases and standardizations for creating and enriching data lenses. This information can be imported into a data lens from an Excel spreadsheet with the use of the Import Phrases and Terms feature.

The format of the Excel spreadsheet or tab-delimited file (CSV or text) can be of your choosing. For example, you could include columns that indicate a phrase, a term for the phrase, and the initial data.

The following examples illustrate how you might create the columns and populate your spreadsheet files:

There are two columns you can use to create term variants as follows:

fullform

Allows for the import of the full-form variant of the term rule.

handling

Allows for the option of creating term variants.

Note:
The fullform and handling columns must be located in your spreadsheet after any text or regex columns and before any standardization columns.

The values that can be used in the fullform and handling columns are as follows:

Expand

Automatically creates term variants of the text node.

Plural

Automatically creates term variants and plural forms.

No or a blank cell

Does not create any term variants; term remains unchanged.

In addition, standardization rules can be imported within the same spreadsheet. the standardization types are Full Description and Short Description.

Note:
Prior to importing rules within certain standardization, the standardization type must present in the data lens.

To import your new knowledge into your data lens:

1. From File menu, click Import Phrases and Terms.

2. Browse to the import spreadsheet and click Open.

The rules are imported into the data lens. The percentage of recognition increases with the addition of these newly imported rules.

Creating Aliases When Importing Rules and Phrases Using Excel

You can create aliases when you import rules and phrases using Excel. There is no 'Alias' column in the Excel spreadsheet so aliases are created automatically based upon the name of the attribute or Item Definition. The Knowledge Studio assumes that the labels you provide in the spreadsheet are aliases, modifying them as needed to create attribute names that abide by the required conventions (for example, changing spaces to underscores in the attribute name).

These alias names can be edited or viewed. See "Aliases".

Exporting and Importing Item Definitions

Importing and exporting Item Definitions from one data lens to another allows you to reuse Item Definition information in new ways.

You can easily export Item Definitions from one data lens and import them into another data lens. You can use this feature to import single Item Definitions or entire branches of an Item Definition hierarchy into a new or existing data lens.

When you import an Item Definition, all rules that participate in the Item Definition are also imported.

1. Open the source data lens.
This is the data lens that contains the Item Definition (or hierarchy of Item Definitions) you want to export out of the source data lens and import in your target data lens.

2. To maximize reuse of knowledge contained in the source data lens, verify that all Smart Glossaries relevant to the Item Definition and all unit conversion knowledge you want to import are contained in the target data lens.
If you need to import a Smart Glossaries, see "Importing a Smart Glossary".

3. Select the Phrases tab and the Define Items sub-tab.

4. Right-click the Item Definition branch that you want to export, and click Export Item Definition.

5. Enter a name for the export file or accept the default filename, and then click Save to save the exported Item Definition(s).

6. Open the destination data lens.

7. Select the Phrases tab and the Define Items sub-tab,

8. Right-click the parent branch into which you would like to import the Item Definition and click Import Item Definition.

9. Select the export file you saved, and then click Open to import the Item Definition.
The Item Definitions and all rules associated with the Item Definitions are imported into your destination data lens.

Troubleshooting Item Definition Export and Import

This section describes some possible issues you may encounter and how to resolve them.

Pre-existing Item Definitions with Same Name as Import

You cannot import an Item Definition that already exists in the target data lens and attempts to do so result in an informational message advising you that it is not possible.

This is best resolved by renaming the Item Definition.

Source Item Definition Relies on Knowledge Contained in an Smart Glossary

If you have exported an Item Definition that relies upon a Smart Glossary (for example, for unit conversion of units of measure), be sure you import the Smart Glossary into the target data lens before you import the Item Definition. If not, you may need to re-create some knowledge in the target lens.

Source Item Definition Relies on Standardization Information

Be sure to create all standardization types needed for the target Item Definition when you import the Item Definition into the target data lens. Standardization information is not preserved when you import an Item Definition.

Source Item Definition Relies on Unit Conversion Information

Be sure to create all unit conversion knowledge in the target data lens when you import any Item Definitions that require unit conversion. Unit conversion information is not preserved when you import an Item Definition.

Source Item Definition Relies on Value Logic Information

Attribute value logic is preserved after the import, provided the target data lens has the requisite unit conversion information. In most cases, unit conversion information is contained in Smart Glossaries, such as DLS_Units_of_Measure or DLS_Units_of_Measure_Retail, so importing the appropriate Smart Glossaries into the target data lens avoids this issue.

Search logic used by attributes of the imported Item Definition is preserved in the import.

Importing Enrichments

You can refine your data using data remediation and enrichment features in EDQP. The process is simply to

- Create or modify a DSA in Application Studio containing Item Definition enrichment and remediation steps.

- Create or modify a corresponding Governance Studio project and use AutoSuggest to augment your data with AutoSuggest and your suggestions. You create a data enrichment task to enable the importation of the enriched data knowledge into your data lens.

- Create or modify a corresponding Knowledge Studio data lens and import the enriched data knowledge into your data lens, and then verify the changes.

Enriched data knowledge can be imported in one of two ways, directly from your Oracle DataLens Server or from a file that you downloaded from an enrichment task. The following sections describe both import methods.

Importing from Your Oracle DataLens Server

This import method is automatically available when you create the data enrichment task in Governance Studio because it is stored in a file on the server. You can select the enrichments that you want to import and view user enrichment suggestions before you import.

1. From the File menu, select Import Enrichments from server.

This dialog remains open so that you can continueto reference the information while refining your data lens.�.

2. Using the check boxes, select the data enrichments suggestions that you want to import from the list in the upper pane. All enrichments that have been created for the data lens, regardless of who created them, are presented.
Any user enrichment suggestions appear in the lower pane so that you can consider adding them to your data lens.

3. Click Import Enrichments.
A confirmation message is displayed indicating the number of enrichments that were imported or any errors if the enrichments were not created.

4. Click OK.

5. Review and modify the new productions added to your data lens. See "Modifying Phrase Productions".
These productions are indicated with a light bulb for valid AutoSuggest enrichments and a person with check mark icon for user suggestions for enrichment as follows:

6. (Optional) Select Reviewed when you are done with these productions to indicate that you have complete your review.

7. Click OK.

Importing from a Downloaded File

This import method relies on you downloading any data enrichment files stored on your Oracle DataLens server to a known location on the user's system by viewing the given task in Task Manager, Application Studio, Governance Studio, or Knowledge Studio. See "Viewing Tasks". The default download directory is in the user's C:\Users\user_name\AppData\Roaming\DataLens\tmp directory.

All enrichments applicable to the open data lens are automatically applied unlike the ability to review and select them as in "Importing from Your Oracle DataLens Server". To review all of the enrichments contained in a data enrichment task before you import them, see "Viewing Data Enrichment Tasks".

1. From the File menu, select Import Enrichments File.

2. Select the file you want to import from the list of downloaded files or locate the file on your system if it is not in the default download directory.
A confirmation message is displayed indicating the number of enrichments that were imported or any errors if the enrichments were not created.

3. Click OK.

4. Review and modify the new productions added to your data lens. See "Modifying Phrase Productions".
These productions are indicated with a light bulb for valid AutoSuggest enrichments and a person with check mark icon for user suggestions for enrichment as follows:

5. (Optional) Select Reviewed when you are done with these productions to indicate that you have complete your review.

6. Click OK.

10 Defining Context and Item Definitions Further

This chapter explains techniques and information related to defining context and Item Definitions further using the Knowledge Studio.

Editing Multiple Phrases and Terms

You can view and modify all of the phrases and terms attributes in your data lens from one dialog box. From the Edit menu, click Edit Phrase and Term Attributes.

Edit the phrase and term attributes using the check boxes and field in the following columns:

Do Not Translate (DNT)

Sets/clears the 'Do Not Translate' attribute for phrase or term rules.

Prohibit Rename (PhbR)

Sets/clears the 'Prohibit Rename' functionality for the selected rule. If the selected rule has this check box selected, then you will not be able to rename it.

Format to Locale (Format)

Sets/clears the 'Format to Locale' attribute for phrase or term rules.

Prohibit Anchor (PhbA)

Sets/clears the 'Anchor' attribute for the select phrase rule. If the selected rule has this check box selected, then this rule cannot be at the top of the phrase structure. For this rule to be invoked, it must have a parent.

Anchor

Sets/clears the 'Anchor' attribute for the selected phrase rule. If this check box is selected, then the selected phrase rule may sit at the top of a phrase structure and does not require a parent node.

Promote Children (PromCh)

Sets/clears the 'Promote Children' attribute for phrase rules. This allows the ability to promote lower level phrases separately.

Translate Variable (TV)

Sets/clears the 'Translate Variable' attribute for phrase and terminology rules. This will allow this phrase and/or term to be translated once regardless of where in context it is used.

Full Form

Displays any full form term variants that have been generated for the term. You can click in this field to add or edit the full form of each term.

Needs Review (NRev)

Sets/clears the 'Needs Review' attribute for terminology rules.

Case Sensitive (CaseSen)

Sets/clears the 'Case Sensitive' attribute for terminology rules.

An attribute can be set or cleared for a range of rows by clicking on the attribute for the first rule, holding down the shift key, and then clicking the attribute for the last rule. This only works within a single attribute column.

If you double-click the rule name, you can open the Review Productions dialog for that rule.

Generating Term Variants

When you define a terminology rule, Knowledge Studio can automatically generate abbreviated forms for that term. For example, if you have a [resistor] terminology rule, Knowledge Studio can automatically generate a variety of possible alternatives, such as rsstr. In many circumstances, this eliminates the need for you to manually drag-and-drop to create additional associations.

The generation of term variants feature is a data lens option that you set. Once you set the data lens option to create automatically generate variants, generation is applied as you create terminology rules. For more information about data lens options, see "Setting Data Lens Options".

For this feature to be effective, you must label the terminology rule with the full name. Using the rsstr example, you must label the terminology rule [resistor] so that Knowledge Studio can create as many variations as possible.

After you finishing building the phrase structure, a blue icon with a check is now displayed next to the term indicating that variants have been defined and that the term requires your review.

You can review automatically generated variants by double-click the term.

Review the variants and delete the variants that you do not want. You can sort the productions in the list by right-clicking on any of them and selection Sort Productions. After you have completed your review of the term variants, you indicate this to the data lens with the Reviewed check box.

Alternatively, you can use the Edit Attributes function to review terminology rules though you cannot mark the term as 'Reviewed' using this feature.

Phrase and Terminology Rule Syntax

This section describes in detail the syntax of the data lens phrase and term rules. You can see a rules detailed structure using the Edit Rule feature.

The Edit Rule menu item is only available from the Define Phrases sub-tab on the Phrases tab and is accessed by right-clicking on a term.

Using the Edit Rule dialog allows you to change the rules that are currently being recognized by the data lens. It is recommended that you fully understand the implications of using this functionality prior to making changes.

The following sections use the Edit Rule dialog to describe rule syntax.

Terminology Rules

A terminology rule starts with the rule name inside of square brackets. The name cannot have spaces and must start with a letter. When you create rules using Knowledge Studio the proper syntax is created for you. In the following example, the terminology rule [resistor] contains variants of the resistor term. In other words, [resistor], is satisfied by RESS, RESP, RESISTOR, etc. The text within the parenthesis represents literal text from the content. For each literal, a leading tab (not spaces) and surrounding parenthesis are required. Each row of a rule is called a production. Each production represents a unique text variation that can be recognized by the rule.

Once a terminology rule has been defined then additional variations can be entered immediately by using the edit rule functionality. See "Generating Term Variants".

The items immediately to the right of the preceding rule name are the rule attributes. In the preceding example, the [resistor] rule has its case attribute set to case insensitive.

Case Sensitivity in Terminology Rules

String matches may be made case sensitive or case insensitive. Case sensitivity can be defined at the data lens level or at the individual terminology rule level.

Case sensitivity for regular expression matching, however, cannot be set as a rule attribute. They must be set in terms of the matching context. See the regular expression definitions and examples in the following section.

Regular expressions in Terminology Rules

A regular expression is a way to capture various text forms in a simple representation. For example, all integers can be represented by the regular expression pattern /\d+/. For a complete discussion of regular expression syntax, see Appendix A, "Regular Expressions." This section describes the use of regular expression in the standardization process.

Rules containing strings of text must be matched exactly to the strings. These strings are identifiable by the fact that they are directly enclosed by parentheses. The following example shows a term rule that contains three different model numbers.

Rather than writing each entry as it appears in the content, you can use the following shortcut that defines a model number as an integer, followed by D, followed by another integer. The integers are of unspecified length. This pattern will match every occurrence of model number of this form (for example, one or more integers followed by a capital 'D' followed by one or more integers) in our data without further action. Regular expressions can be entered into a terminology rule through the edit rule functionality of the Knowledge Studio.

If you enter the text '10D08' you would see that it is recognized by the [model_number] rule as in the following example:

White Space, Regular Expressions, and Terminology Rules

There are two forms for regular expression rules within a term rule:

- Regular expressions that are not sensitive to white space. These are defined in terminology rules using the forward slash (/) match characters. You can use this regular expression format if you want to recognize terms that are embedded in other terms, i.e. terms that are not surrounded by white space.

- Regular expressions are sensitive to white space. You can also define regular expressions bounded by white space. You typically use this technique for names and addresses, especially when embedded white space is important to identifying your text items. To do this you use curly brackets ({}) to define your regular expression. Alternatively, you could use the source formatting feature to add white space to regular expressions as described "Source Format".

The following example matches model numbers, but only when the model number is surrounded by white space.

Note:
The use of curly brackets to delineate white space separated terminology is not a Perl standard for regular expressions. The use of curly brackets within the Perl standard and SCS-defined curly brackets are not in conflict. For example, the following terminology regular expression matches model numbers that have from one to three of the capital letter 'D' surrounded by two integers, and the model number as a whole is both preceded and followed by white space:
{\d{2}D{1,3}\d{2}}

Phrase Structure Rules

Phrase structures are composed of phrase rules and term rules. Phrase rules contain productions which are composed of term and other phrases.

The top most phrase structure rule must start with a greater than symbol, '>'=. In the following example, each line of the rule represents a different form of [sae_thread_size]. The first form, for example, consists of a [screw_dimension] (which is itself a phrase structure), a [separator_dash], and a [real]. Each line is in parenthesis with a space between each phrase structure rule. Each phrase structure will eventually reference one or more terminology rules. Other formatting is the same as for terminology rules.

[sae_thread_size] references [screw_dimension] as follows'

[screw_dimension] in turn references [inch_attribute] as follows:

[inch_attribute] in turn references [size_unit] as follows:

[size_unit] finally references the terminology rule [inch], which defines double-quote to be inch as follows:

Start Symbols in Phrase Structure Rules

Phrase structure rules can include a special character called a start symbol, >. Start symbols indicate the root of a phrase structure.

Phrase structure rule names that include the start symbol appear as tags in the tagged content file.

Other Constraints on Rules

Rule names must be unique. It is recommended that you use a rule naming convention that represents the product and attributes of your content. Additional points to consider:

- A phrase structure rule can only contain the names of term rules.

- A terminology rule can only contain literal text or regular expressions.

- A rule name can only contain alphabetic, numeric, and underscore characters.

- White space in literal text is meaningful. (J L) and (JL) are not equivalent.

- Rule names cannot start with numbers.

Global Phrase Rule Renaming

The ability to rename a phrase rule and apply this change in knowledge throughout your data lens can be a powerful way to ensure consistency and consolidate rules. The Rename Rules functionality available from the Edit menu provides more search functionality than the context-sensitive option of the same name.

This renaming operates similar to a typical search and replace. You can choose to add a prefix, suffix, or to replace information for the selected rule; only one of these renaming functions can be selected.

If you choose to add a prefix or suffix to the rule, you simply enter the text in the field and click OK.

Replacing information in the rules provides several self-explanatory choices for searching and replacing the data. You cannot leave both the Replace and With fields blank though if either is left blank it operates as a deletion based on the other selected options.

For example, if you could use the At start/end of name, Before, and After options to specify where in a rule you want to make changes. Using the options as follows:

Replace

res_

With

resistor_

After

a_

The previous example will change 'a_res_ohms' to 'a_resistor_ohms' and will not change 'a_resolution_pixels'.

Source Format

Source Format allows the content to be reformatted prior to the creation of knowledge. The purpose of the formatting is to reduce the number of special rules for content standardization. Source formatting is only needed prior to the application of the data lens rules.

For example, the following figure shows the text 'Res10Ohm|1\8W|' entered into the Selected Content field, directly below the Graphical Rule Building pane. In this case the data contains pipe (|) field separators. The Knowledge Studio is not able to separate the '|' from the terms. If a problem like this is found repeatedly in the sample data, then you should apply Source Formatting.

Applying Source Formatting

1. From the Data Lens menu, click Source Format, and select Standard Mode.

2. Enter a comma in the entry field of the Edit Source Format Rules dialog and click OK.
For example, if you needed to add spaces around all of the commas in your data, you would enter &rsquor;,' (a comma).

3. Click OK.

4. To apply the new source formatting to your line of text, click in the Sample Data pane and press Enter.

5. Click Refresh to refresh your source formatting changes across your entire sample data file.

The phrase structure display is updated in the Graphical Rule Builder pane as follows:

From this point forward all ',' characters are surrounded with spaces, allowing you to build the phrase structure that you need. If you want to turn off source formatting for a particular character, simply reverse the process.

Note:
If the comma is followed by another character, that character is formatted too. In addition, multiple characters next to one another operate as a list.

The Expert Mode option of the Source Format feature can be used for removing, substituting, and adding characters to your source input. An example of a regular expression used in Expert Mode is as follows:

This regular expression substitutes all instances of the character ';' with the same character with spaces on either side of it, '; '.

Compacting Grammar

You can remove any grammar rules (terms and phrases) that are required by the data file against which the compaction is run.

1. From the Data Lens menu, click Compact Grammar.

2. Review the messages and ensure that you have the sample data you want to use to compact the grammar.

3. Click Yes.

4. Select the sample data file and click Open.
Your data lens grammar is then compacted and the results are displayed for you to review.

5. Click OK.

11 Standardizing Data Further

This chapter explains techniques and information related to the standardization of product data using the Knowledge Studio.

Term Standardization

The following sections explain how to use rules to further standardize your terms.

Case Replacement

You have the ability to override the case of string replacements. For example, if all string replacements have been set to lower case, and you want to switch to all upper case, you can do this quickly by selecting the Case option in the string replacement tab.

For example, if the term 'resistor' was originally replaced with a proper cased 'Resistor', you can change the term to replace all data with uppercase using the Case option of the terms Rewrite rule.

The result of changing this setting is that all instances of the 'resistor' term are changed to upper case.

Regular Expression Replacement

Regular expression replacement is meant for complex string replacement when the text being replaced may be a variable. The example in this section shows how you can handle an intelligent conversion of two digit years to four digit years and place the years in the correct century based on a predefined boundary.

Suppose that there is a defined rule for two digit years and you want to convert the two digit years to four digit years. To place the years in the correct century, all years from 29 and below should be placed in the 21st century, and all two-digit years 30 or above should be placed in the 20th century.

First, you would create a terminology rule for year using a regular expression as follows:

Next, you would create a regular expression string replacement to make the two to four digit years similar to the following:

Note:
This example strings together multiple regular expression replacements to achieve the desired result. Singular regular expression replacements can be used by separating each expression with a semi-colon, ';'.

The example string replacement results in the following standardization result of two digit years:

As expected, the number 20 preceded all two-digit years less than 30, and the number 19 proceeded all two-digit years over 30. See "Regular Expressions".

Individual Replacement

When performing individual replacements of terms, the case sensitivity of the terminology rule is honored by the replacement rule. For example if the term rule is defined to be case insensitive (the default), and the replacement rule is as follows:

The preceding replacement rule replaces both 'RES' and 'res' with the text 'Resistor'.

Multiple Standardization Types

You can configure as many standardization types as needed using the Standardization Types dialog. The benefit is that the phrases and terms defined in a single data lens project can be reused to define any number of standard results.

For example, you may want to have a standardization type that creates a very readable long form description, without abbreviations, for use on your web site. You may also need a short form standardization type that creates an abbreviated description that conforms to the character length limits imposed by a database table.

See "Creating a Standardization Type".

Unit of Measure Standardization Types

If the standardization requirements need the data to be converted to a single unit of measure this can be handled by using a unit of measure conversion type.

If your data is in multiple units of measures that are defined to terminology and phrase rules, then these rules can be defined to take the different unit of measures and convert them to a single unit of measure to provide a standard description.

For example, data for [a_ec_dimension_inch] can be in inches or foot. When the data is standardized, the required unit of measure is inches. The text is parsed to the phrase to be converted from 'foot' to 'inches' as in the following.

You can create your own standardization schemas to be used throughout your data lens. Standardization types can be used for a multitude of uses.

Note:
If you change the default integer, fraction, decimal rules that are created by the Knowledge Studio with each data lens, it causes the unit conversion to fail. For example, adding the text 'one' to the [integer] rule.

Creating a Unit Conversion Type

1. From the Data Lens menu, select Unit Conversion Types….

2. Click the Add New button.

3. Enter the requested information to create your new unit conversion type that will be added as a selection option to the Unit Conversion Types list.

4. If you already have a unit conversion type created and you want to reuse that knowledge in a new version of the same standardization, select the Base type on other type check box, and then select the appropriate classification type from the Based On: list.

Note:
This check box is not active if there are no other unit conversion types.

5. Click OK.

Like standardization types, there can be multiple unit of measure conversion types associated with a data lens.

Deleting Unit Conversion Types

You can delete standardization types if necessary.

1. Ensure that you have checked in your latest data lens version.

2. From the Data Lens menu, select Unit Conversion Types….

3. Select the standardization type that you want to delete, and then right-click on it.

4. Click Delete Unit Conversion.
A deletion verification dialog is displayed.

5. If you want to delete the selected unit conversion type, click OK otherwise click Cancel.

6. Click OK.

Creating a Unit Conversion for a Phrase

When creating a unit conversion for a phrase, you must have a number and a unit in every production that you want to unit convert or the conversion will fail.

1. Select the Standardize tab and the Unit Conversion sub-tab.

2. Select a phrase production that requires a unit of measure conversion.

Note:
Phrases that do not have a Unit of Measure Standardization type associated with them have a round, blue icon next to the phrase. Phrases that have a unit of measure conversion have a round, purple icon. Parent phrases of converted productions change from red boxes to round, yellow icons.

3. Click Next to begin creating your unit conversion table

4. Click Next to accept the selection of the rule [number] for Number and [u_length] for the Unit to convert.

5. Select an existing table name or enter in a new table name.

6. If this information is correct, click Next. If not, select the correct table that should be used from the drop down box for this phrase, or create a new table for selection.

7. Click Next to advance the wizard.
If a table with pre-existing unit of measure conversions is select, they are displayed in the table; otherwise, a blank table appears.

8. If a new table is created, then the new unit of measure conversions, you must create a units table using the Add Row and Remove Row buttons.

9. Enter a unit name when the dialog is displayed and then provide a conversion factor in the relative size field.

10. Complete all rows as required and then click Next.

11. Select the target unit of measure.
For example, if the data contains feet or foot and needs to be converted to inches then the target [inch] should be selected.

12. Click Next.

The icon next to the phrase should change to purple to indicate that a unit of measure standardization type has been associated with the phrase and a red box will precede the associated phrase rule.

To test the unit of measure standardization, select the Test Standardization sub-tab and enter text that should be converted.

The unit of measure conversion should convert the text entered into the correct standardization unit of measure.

Turning on unit conversion allows the use of ranges under standardized attributes, as well as, value and search logic in an Item Definition. The unit conversion must be created to realize these benefits though it does not need to be selected. For example, if you want to output fractions, do not disable Unit Conversion rather set it to none so that value logic and ranges still operate properly.

Share Standardizations Within a Data Lens

The effort to create standardization knowledge can be substantial, depending on your data so the ability to share (reuse) your standardization rules saves labor costs because it avoids inputting the information repeatedly in various Standardization Types and Item Definitions. While the ability to create a Standardization Type based on an existing one is useful, sharing standardization knowledge after it has been refined takes the next step in easily defining your data further.

The following sections describe how to copy:

- global standardization rules between Standardization Types

- standardizations rules of an Item Definition to another Standardization Type

- standardizations rules between Item Definitions

Throughout these sections the following terms are used:

Source

The Standardization Type or Item Definition that contains the knowledge (standardization rules) that you want to copy.

Target

The destination Standardization Type or Item Definition that you want to receive a set of selected knowledge.

Scope

How you want to copy the knowledge from the source to the target; new only, merge, or replace. Each copy operation causes the Knowledge Studio to inspect both the source and target from the highest level (phrase or parent Item Definition) to the lowest possible level (production or attribute) to determine the differences in each rule, production and table (if relevant).

Contents

What standardization rules you want to copy from the source to the target which you select from a list. For example, data originally standardized to lowercase is easily changed to upper case by changing the case setting rule in one Standardization Type and copying it to the other Standardization Types in your data lens thus this change is quickly effected.

Copying Global Standardizations

You can copy the global standardization rules that you have refined in one Standardization Type to another in your data lens.

This option can be used on any tab in Knowledge Studio because the current Standardization Type is the source and you select the target Standardization Type that the standardization rules will be copied to.

1. From the Standardization Type list on the toolbar, select a source Standardization Type.

2. From the Data Lens menu, click Copy Global Standardizations.

3. Select the target Standardization Type from the list.

4. Select one of the following copy scope options to copy the global standardization rules from the selected Standardization Type:

- Copy new only
Only the standardization rules for the contents (selected in the next step) of the source Standardization Type that exist in the source Standardization Type and do not exist in the target Standardization Type are copied. In other words, the standardizations that are 'new' to the target Standardization Type are copied.

- Merge
The standardization rules for the contents (selected in the next step) of the source Standardization Type are merged with the target Standardization Type. Only standardization rules that do not exist in the target Standardization Type are copied. For example, new productions and entries in a rewrite table are copied. If conflicts are encountered, the rule is ignored and is not copied.

- Replace
All standardizations for the contents (selected in the next step) of the source Standardization Type are copied to the target Standardization Type overwriting existing standardization rules.

If the Knowledge Studio detects that you are attempting to copy a blank source Standardization Type (contains no rules) to overwrite a target that contains rules, a message is displayed that it is not possible and the copy is terminated. Review the source and target Standardization Types to ensure that you identified them correctly.

5. Select the contents of what you want to copy with the Standardization Type using the check boxes as follows:

- String Replacement
All individual string replacement rules in all text replace tables including no replacement, replace all, regular expressions replacement, and individual replacement tables, rewrite rules that appear in the Standardize Terms sub-tab of the Standardize tab.

- Case
All individual case replacement rules that appear in the Standardize Terms sub-tab of the Standardize tab. This does not include the default case setting for a Standardization Type.

- Phrase Rewrite/Ordering
All term and phrase ordering rules that are defined in the Standardize Phrases sub-tab of the Standardize tab.

- Numeric
All numeric text replacement rules in all value range rewrite rules tables that are defined in the Standardize Terms sub-tab of the Standardize tab including text and conversion tables.

- Join
All term joining rules at the phrase level defined in the Standardize Phrases sub-tab of the Standardize tab at the phrase level not the production level.

6. Click OK.
The global standardization rules are copied from the source to the target Standardization Type using the scope and contents you selected. A confirmation message detailing the changes is displayed upon completion.

Select and review the target Standardization Type to ensure that your rules were copied correctly.

Sharing Item Definitions Standardizations

You can share Item Definition standardization rules with other Item Definitions both within and across Standardization Types. This is generally performed after you have created and standardized an Item Definition. This functionality also allows you to continue to modify your Item Definition and copy the standardization rules to other Item Definitions and Standardization Types.

This feature relies on the existence of the same attribute, phrase, and production structure in both the source and target Item Definitions. Copying standardization rules from between Item Definitions does not create attributes in the target that exist in the source. In the following example, the Highlighters and Mechanical_Pencils Item Definitions both contain a Size attribute with the production, [a_length]. The standardization rules for Size and [a_length] can be copied from one Item Definition to the other.

The standardization rules for the productions of a phrase in an attribute that exist in both Item Definitions can be shared. In the following example, the standardization rules for the productions of [a_length] in Mechanical_Pencils can be shared with Highlighters.

There are three options for copying standardization rules from one Item Definition to another:

Copy to another Standardization

Copies standardization rules from a source Item Definition in the currently selected Standardization Type to a target Item Definition in the target Standardization Type. Only the parent Item Definition is copied even if it contains child Item Definitions. You can select the attributes you want to copy when the selected Item Definition is not a parent.

Copy with Children to…

Copies standardization rules from a parent Item Definition and all of its child Item Definitions in the currently selected Standardization Type to the same set of Item Definitions in the target Standardization Type. You can select the attributes you want to copy when the selected Item Definition is a parent.

Copy to another Item Definition

Copies standardization rules from a source Item Definition to a target Item Definition within the currently selected Standardization Type. Only the standardization rules for those attributes, phrases, and term rules that are common to both Item Definitions are copied. You can select the attributes you want to copy when the selected Item Definition is not a parent.

The scope and depth of the standardization rules that you copy are selective by you.

Tip:
You can copy inactive Item Definitions and activate them in the target Standardization Type or Item Definition.

Copying Item Definitions Between Standardization Types

1. From the Standardization Type list on the toolbar, select a source Standardization Type.

2. Select the Standardize Items tab and the Standardize Attributes sub-tab.

3. Right-click the Item Definition that you want to copy, and then select the Copy to another Standardization or Copy with Children to… option.

Note:
If you select an Item Definition that has no children, the Copy with Children to… option is not available; the options are context-sensitive.

The use of these two dialogs is the same though the extent that the source Item Definition is shared is different as previously described.

4. Select the target Standardization Type that you want to copy the source Item Definition to from the list of Standardization Types.

5. Select one of the following options to define how the source Item Definition is copied:

- Copy new only
Only the standardization rules for the contents (selected in the next step) that exist in the source and do not exist in the target Item Definition in the target Standardization Type are copied. In other words, the standardization rules that are 'new' to the target Item Definition in the target Standardization Type are copied.

- Merge
The standardization rules for the contents (selected in the next step) of the source Item Definition in the source Standardization Type are merged with the target Item Definition in the target Standardization Type. Only standardization rules that do not exist in the target are copied. If conflicts are encountered, the rule is ignored and is not copied.

- Replace
All standardization rules for the contents (selected in the next step) of the source Item Definition in the source Standardization Type are copied to the target Item Definition in the target Standardization Type overwriting existing standardization rules.

Note:
The Item Definition attribute ordering and match ranking standardizations defined on the Order Attributes and Match Weights sub-tabs of the Standardize Items tab are not included when coping Item Definitions.

6. Select the scope of what you want to copy with the Item Definition using the check boxes as follows:

- String Replacement
All individual string replacement rules in all text replace tables including no replacement, replace all, regular expression replacement, and individual replacement tables, rewrite rules that appear in the Standardize Attributes sub-tab of the Standardize Items tab.

- Case
All case replacement rules that appear in the Standardize Attributes sub-tab of the Standardize Items tab.

- Phrase Rewrite/Ordering
All term and phrase ordering rules that are defined in the Standardize Attributes sub-tab of the Standardize Items tab.

- Numeric
All numeric text replacement rules in all value range rewrite rules tables that are defined in the Standardize Attributes sub-tab of the Standardize Items tab including text and conversion tables.

- Join
All term joining rules at the phrase level defined in the Standardize Phrases sub-tab of the Standardize tab at the parent phrase level not the production level. This check box is active when the Copy Global Standardizations check box is not selected.

7. If the selected Item Definition is not a parent, then you can choose to select which attributes you want to copy to the target Item Definition by selecting the Limit Which Attributes are Copied check box. This activates the list of all required, scoring, and optional attributes though does not select any.

Press and hold Ctrl and click on each attribute to select it for inclusion. Only attributes that are selected are copied.

Note:
When copying parent Item Definition attributes to children Item Definitions, ensure that attributes of the same name are not unique semantically before you attempt to copy. For example, 'Size' could be in both Item Definitions though the parent is standardized to use millimeter and the child is standardized to centimeter thus the Item Definitions are semantically unique.

8. Select the Copy Global Standardizations check box to copy the global standardization rules within the source Item Definition based on your scope and contents selections. This option is "sticky" so your last selection is redisplayed each time you use this dialog.

Caution:
When selecting this option and no changes have been made by the copy standardization, then the global copy changes are terminated and the copy will fail. This is because it is risky to copy global changes when nothing was changed at the Item Definition level. Review what your intent was with copying the Item Definition standardization rules. It is possible to force the Global Standardizations to copy, see "Copying Global Standardizations".

9. Click OK.
The source Item Definition standardization rules in the source Standardization Type are copied to the target Item Definition in the target Standardization Type using the scope and contents you selected. A confirmation message detailing the changes is displayed upon completion.

Select the target Standardization Type and Item Definition, and then review the standardization rules. Verify that the results you wanted have been achieved.

Caution:
When using the Replace option, if you are attempting to replace more than 20% of the source standardization rules in the target Standardization Type a warning is displayed. This is intended to aid you in avoiding overwriting existing rules in the target and losing valuable standardization knowledge.

You should closely review your source and target copy results as it is possible to inadvertently overwrite standardization rules that you want to keep.

Copying Between Item Definitions

1. From the Standardization Type list on the toolbar, select a source Standardization Type

2. Select the Standardize Items tab and the Standardize Attributes sub-tab.

3. Right-click the source Item Definition that you want to copy from, and then select the Copy to another Item Definition option.

4. Select the target Item Definition that you want to copy the source Item Definition to from the list.

5. Select one of the following options to define how the Item Definition is copied:

- Copy new only
Only the standardization rules for the contents (selected in the next step) that exist in the source and do not exist in the target Item Definition are copied. In other words, the standardization rules for a production of an existing phrase in an attribute that are 'new' to the target Item Definition are copied.

- Merge
The standardization rules for the contents (selected in the next step) of the source Item Definition are merged with the target Item Definition. Only standardization rules that do not exist in the target are copied. If conflicts are encountered, the rule is ignored and is not copied.

- Replace
All standardizations rules for the contents (selected in the next step) of the source Item Definition are copied to the target Item Definition overwriting existing standardization rules.

Note:
The Item Definition attribute ordering and match ranking standardizations defined on the Order Attributes and Match Weights sub-tabs of the Standardize Items tab are not included when coping Item Definitions.

6. Select the scope of what you want to copy with the Item Definition, as defined on the Standardize Attributes sub-tab of the Standardize Items tab, using the check boxes as follows:

- String Replacement
All individual string replacement rules in all text replace tables including no replacement, replace all, regular expressions replacement, and individual replacement tables, rewrite rules.

- Case
All case replacement rules.

- Phrase Rewrite/Ordering
All term and phrase ordering rules.

- Numeric
All numeric text replacement rules in all value range rewrite rules tables including text and conversion tables.

7. If the selected Item Definition is not a parent, then you can choose to select which attributes you want to copy to the target Item Definition by selecting the Limit Which Attributes are Copied check box. This activates the list of all required, scoring, and optional attributes though does not select any.

Press and hold Ctrl and click on each attribute to select it for inclusion. Only attributes that are selected are copied.

8. Click OK.
The source Item Definition rules are copied to the target Item Definition using the scope and contents you selected. A confirmation message detailing the changes is displayed upon completion. The Item Definition pane is refreshed and any expanded Item Definitions are collapsed.

Select the target Item Definition and review it to ensure that it was copied correctly.

12 Classifying Data Further

This chapter describes techniques and information related to the classification of data using the Knowledge Studio.

Advanced Classification Rule Functions

This section describes the various classification functions that you can use to narrow the classification of your data, including an example.

Addition

The Addition function is intended to include two or more grammars whose union defines the classification of the item.

For example, 'Power Nail Stapler' versus 'Paper Stapler'. Stapler may be enough to classify the office product Stapler though an additional attribute is needed to correctly classify Power Nail Stapler as a power tool. So you would include Nail with the item Stapler.

Masking

The Masking function is intended to disqualify all grammars below the masked phrase. It is typically used for inclusions that are not part of the primary item to be classified.

For example, 'Drill with Charger'. Here the item is Drill and not Charger. An example follows showing the use of Masking and the associated grammars.

Negation

The Negation function is intended to disqualify all grammars where the inclusion or preposition is implied but not stated.

For example, 'Toner Cartridge HP Printer'. The item is a Toner Cartridge not a printer.

Parent

The Parent function is intended to reference a grammar at a higher level in the classification tree. Its application is to apply inheritance from the high level to a lower level where other discriminating attributes are defined.

For example, resistors contain both Fix and Variable types. The [product_resistor] term would reside at the resistor level in the schema and variable or fixed would reside at a lower level in the tree. The connection between [product_resistor] and [attr_variable] is through the term $parent + [attr_variable] where $parent references [product_resistor. This is useful for bulk classifying data at a higher level to get an initial classification, and then refining the classification at a later stage.

Function Example

This example shows a collection of items that pose complications in classification. The use of the previously described functions removes ambiguities allowing each item to be uniquely classified.

Add Mask

To add masking drag the root level grammar to be masked over the mask icon at the top of the classification tree.

All grammars that appear under this grammar will then be hidden from further classification. Any grammar that is hidden under the masked grammar though is visible in other phrase structures can be used for classification.

Add Negation

To add negation, hold down the control and shift keys together while dragging the negated phrase next to the associated primary class item.

Add a Parent

To add a parent, use the following steps:

1. Drag the primary item to the upper level classification node

2. Drag the secondary item to the lower level classification node.

3. Right-click on the secondary item and select Add Parent.

Classifying to Several Schemas in a Data Lens

You can configure as many classification types as you need using the Classification Type feature.

For example, you may want to classify data to an UNSPSC schema and simultaneously to an eCl@ss schema or to a user-defined schema.

You should apply the following considerations when using several schemas in a single data lens:

- When creating a name for the new classification type, you should include the classification version number information in the name to enable differentiation. For example, when using UNSPSC 11.1, use a name that is similar, like UNSPSC_11_1.

- To reuse the rules already created in a previous classification type, select the Base classifications on other classifications check box, and then select the classification type on which you want to base the new type.

See "Classification Type".

Upgrading a Classification Type

At some point, you may have the need to upgrade from one classification type to a newer version of that same classification type. For example, you can upgrade from UNSPSC classification version 9.2 to the newer version 11.1. You can upgrade to a newer classification type and retain all of the knowledge in the previous version.

Upgrading from one classification type to a new type requires basing the new type on the existing type. When using the process to create a new classification type, ensure that you select the Base classifications on other classifications check box so that you can select the appropriate schema to base the new type on from the Based On: list. See "Classification Type".

If Knowledge Studio encounters a classification mapping that existed in the previous version that no longer exists in the new version, a message is display that indicate the nature of change in category structure.

Both classification schemas are loaded and you can toggle between the two by using the black arrows.

Creating User-Defined Classification Types

The Knowledge Studio allows you to create your own custom schema that can be used to auto-classify; this is known as a User-Defined Classification Type. You can use one of the template schemas as a master classification file when creating your new classification type or you can modify the examples in Excel directly to add your data. You must use the correct header row as shown from one of the preceding templates.A set of Excel (.cvs) templates are delivered with Knowledge Studio as follows:

UserDefined_Parent_Child_IDs_Template

Defines parent and child Item Definitions by ID.

UserDefined_Parent_Child_Names_Template

Defines parent and child Item Definitions by name.

UserDefined_All_Levels_Names_Template

Defines all levels of an Item Definitions by name.

UserDefined_All_Levels_Names_IDs_Template

Defines all levels of an Item Definitions by name then ID.

UserDefined_All_Levels_IDs_Template

Defines all levels of an Item Definitions by ID.

UserDefined_All_Levels_IDs_Names_Template

Defines all levels of an Item Definitions by ID then name.

These Excel template files are installed on the user's system in the C:\Users\user_name\AppData\Roaming\DataLens\system\schemas directory.

Creating a User-Defined Schema Using Excel

The following example creates a parent and child id schema:

1. Open Excel to a new worksheet or open the UserDefined_Parent_Child_IDs_Template.cvs template file.

2. In the first row, ensure that the first row of the columns A - C are Parent, Category, and Description respectively as in the following:

3. Enter your schema hierarchy into each column as appropriate. The Parent column can be left blank for the highest tree nodes; however, it must be entered for all children nodes as in the template.

4. Save the spreadsheet as a comma delimited file, from the File menu, select Save as….

5. From the Save as type list, select CSV (comma delimited) (*.csv).

6. Enter a file name and click Save.
The schema file you have just created is saved as a comma delimited file. You can use it to create a new classification type. See "Classification Type".

Creating a User-Defined Schema Using a Text Editor

You can use any text editor to create a comma-delimited file that contains the same information as described in the previous section. Ensure that the first line of this file must contain the following header information:

Parent, Category, Description

A simpler Parent/Category schema can be created in the same manner if you have a classification with no codes or the category is the code.

Creating a User-Defined Classification Type

You create a new User-Defined Classification Type as would any other type though you choose the comma delimited file that you created as the Master Classification File. The following is an example of what a user-defined schema might look like:

For information about creating Classification Types, see "Creating a Classification Type".

Global Classification Schemas

You can create a user-defined schema that is global. This allows the update by a single user of the classification schema and the changes are made available to any data lens that is using that user-defined classification schema.

When adding a new user-defined classification type, ensure that you select the Global Classification File check box.

For information about creating Classification Types, see "Creating a Classification Type".

Classification System Support

The Knowledge Studio supports extensions to the UNSPSC part classification system at the vendor specific level (level 5).

13 Smart Glossaries

A Smart Glossary is set of semantic knowledge (phrases and terminology) that can be imported into other data lenses.

Importing a Smart Glossary

Importing Smart Glossaries is an excellent reuse feature. When you import a Smart Glossary your data lens quickly and efficiently gains the phrase and term rules contained in the Smart Glossary. If the Smart Glossary is modified and you import the Smart Glossary again, term and phrase rules in your data lens (whether you have modified them since the import or not) will not be changed. Some standardization rules may be affected, depending on the options you select, as explained later in this section.

You can import a Smart Glossary into a new or existing data lens using the following steps:

1. From the File menu, click Import Smart Glossaries.

2. Select one or more of the Smart Glossaries listed. You can use the Ctrl key to discontinuously select items from the list.

3. Use the options to choose one of the following Standardization Options:

- Import new standardization rules only
This option merges new term and phrase rules and new standardization rules from the Smart Glossary with the rules that are already in your target data lens. If you import it again it does not overwrite changes you have made in your target data lens. This is the default.

- Merge new standardization productions
This option imports new standardization rules and adds new standardization productions to your target data lens that have been added to the Smart Glossary since last import into your target data lens. If you import it again, the changes in standardization productions in your target lens are preserved.

- Replace all standardization rules
This option implements global changes in standardization rules for your target data lens.

4. Click OK.
The Smart Glossary is imported into your data lens and the knowledge is applied.

Creating a Smart Glossary

You can create a new Smart Glossary using the following steps:

Note:
Creating importable Smart Glossaries is an advanced feature. If you receive error messages that you cannot debug when you import the new Smart Glossary.

1. Open the data lens you want to designate as a Smart Glossary.

2. On the Data Lens menu, click Data Lens Options.
The Data Lens Options dialog is displayed.

3. Select the Importable Lens check box so that this Smart Glossary can be imported into other data lenses.

4. Click OK.

5. Check-in the data lens to the Oracle DataLens Server.

The data lens is now importable into other data lenses as a Smart Glossary.

Included Smart Glossaries

This section describes the Enterprise DQ for Product Smart Glossaries included in the software release. Smart Glossary files are identified with the DLS_ prefix. Item Definitions have not been used in the Smart Glossaries though they can be imported into data lens that use Item Definitions.

All Smart Glossaries have undergone extensive testing over a large variety of data to enable recognition of the most common relevant forms across the majority of data sets. For your specific data, however, a SME should review recognition output in order to assure results are correct for your purposes.

Colors

The Colors Smart Glossary (DLS_Colors) is designed to help you quickly recognize colors and color families. Colors are organized into color families. A ”Basic Colors” standardization type is provided that allow you to standardize color terms to color families. For example, the color cerulean would be standardized to the blue color family.

The ”Basic Colors” standardization type standardizes each color to one of 11 color families.

Counts

The Counts Smart Glossary (DLS_Counts) is designed to help you quickly recognize counts of specific items such as legs or outlets.

The Counts Smart Glossary recognizes different types of counted items that appear in domains such as electronic components, retail, lighting, and domestic appliances. This smart glossary recognizes integers from small values (such as '2') to large values (such as '12,000'), as well as, alphabetic representations of integers from 'one' to 'twelve'.

The following are examples of the forms recognized:

- 3-way

- 5 door

- three tier

- 2sided

Terms not included in this smart glossary are those that are found in DLS_Product_Packaging, such as 'pair', 'item', and 'count'.

Variants for the terms used in this lens are reasonable abbreviated forms as well as likely misspellings.

One known ambiguity has been identified. If the data contains a part number followed by a counted term that appears in DLS_Counts (for example, 'UPC: 123123123 door'), the part number will not be properly recognized. This is easily fixed by removing the improper phrase structure rule from DLS_Counts.

Materials and Finishes

The Materials and Finish Smart Glossary (DLS_Materials_and_Finishes) recognizes a set of common manufacturing materials, such as metal, natural fibers, and plastics and methods of finishing these materials. You may augment this grammar with the particular materials and finishes used in your product data.

Product Packaging

The Product Packaging Smart Glossary (DLS_Product_Packaging ) recognizes a set of common packages, quantities, and units used to describe packaging for sale of merchandise. The data lens has been tested against products in a large selection of markets for packaged goods including office supplies, tissues, biowaste disposal products, toys, paper products, household supplies, food, garden supplies, and hand tools.

The Product Packaging Smart Glossary recognizes different types of packaging and all combinations of those packaging types, such as tubes per box, boxes per carton, tubes per carton, boxes per case, and so on. It recognizes numerical quantities with and without comma separators ("12,000 or "12000), alphabetical numbers from one to twelve, and alphabetical quantities such as 'pair', 'dozen', 'ream', and 'gross'.

This Smart Glossary recognizes two levels of packaging:

- Units per package, such as '18 tubes per box'

- Packages per container, such as '28 boxes per case'

All units are standardized to numerals.

If your data requires text-based quantity terms or package types not included in the Smart Glossary rule set, you can easily modify existing rules to accommodate these.

While this Smart Glossary is designed to maximize recognition of packaging units, some items represent packages in one domain and items or products in another. For example, paper products are sometimes produced in sheets that are packaged in pads. If pads represent items rather than packaging, you could easily modify the target data lens to exclude 'pad' as a package type.

This Smart Glossary does not recognize pricing information. Prices are commonly excluded from input data. If you want to accommodate price information in data that includes packaging for sale information, you could add a phrase rule that includes both pricing information and packaging quantity information to differentiate these two types of information. The best practice is to eliminate the price information from data sets.

This smart glossary does not recognize units that are quantified by weight such as '14 ounces per box'. It does not generally recognize mathematical-formula style descriptions such as 'bags per box [=] 15 boxes per case [=] 12'.

Units of Measure

The Units of Measure Smart Glossary (DLS_Units_of_Measure) provides you with a quick start on detecting the most common units of measure with minimal effort.

This Smart Glossary recognizes a broad range of common units of measure to serve a large number of target markets, including:

- Time

- Length and distance

- Voltage

- Resistance

- Tolerance

- Data and data rates

- Sound

- Wire gauge

- Temperature

The Units of Measure Smart Glossary also accommodates unit conversion if you need to convert between units of the same type, such as the following:

- Length and distance - Meters to feet or inches

- Volume - Liters to gallons or quarts

- Power - Kilowatts to watts

- Resistance - Ohms to kilohms

A number of unavoidable conflicts of terminology or their abbreviations exist within the Smart Glossary for Units of Measure. This means that after import, you might need to either delete some rules or augment the rule set (using additional rules or using Item Definitions) to uniquely identify the desired units in your data as explained in this section.

There are a number of standard abbreviations that are not included in the Smart Glossary to avoid ambiguities with other terms that share the same abbreviation:

- M - Used as an abbreviation for megabytes or megabits; applies only to meters

- W - Used as an abbreviation for 'width' and for 'watts'; is not included

- L - Used as an abbreviation for 'length' and 'liters'; is not included

- F - Used as an abbreviation for both Fahrenheit and Farad; is included as an abbreviation for Fahrenheit only

Additionally, your data may include product numbers or product codes that could be detected as units of measure. You can correct this with minimal refactoring of the target data lens, using strategies such as removing unused productions from rules, removing line-initial and line-final quotation marks, or using Item Definitions to differentiate items in their context.

In addition, while C and F are recognized in this smart glossary as abbreviations for the temperature scales Celsius and Fahrenheit, this may occasionally cause unintended results. You can correct this easily by such methods as removing the abbreviations where they are not needed or employing value logic within Item Definitions to rule out invalid temperature ranges.

Units of Measure Retail

The Units of Measure Retail Smart Glossary (DLS_Units_of_Measure_Retail) contains the units of measure commonly found in retail data, as more fully described in the next section. For recognition of more specialized units of measure, such as farads, picofarads, joules, microhenrys, or awg values, users should import the standard units of measure Smart Glossary, DLS_Units_of_Measure. Use this smart glossary to recognize units of measure in retail data without adding extra term and phrase rules for less common units of measure.

Some of the types of units this smart glossary recognizes are:

- Amperage

- Data Rates

- Energy

- Length

- Power

- Temperature

- Time

- Voltage

- Volume

- Weight

This Smart Glossary is designed for use without DLS_Units_of_Measure. If you import DLS_Units_of_Measure_Retail into a data lens into which you have previously imported DLS_Units_of_Measure, the hierarchical structure of the DLS_Retail_Units_of_Measure may combine with the hierarchical structure of DLS_Units_of_Measure.

In addition, while C and F are recognized in this Smart Glossary as abbreviations for the temperature scales Celsius and Fahrenheit, this can occasionally cause unintended results. You can correct this easily by such methods as removing the abbreviations where they are not needed or employing value logic within Item Definitions to rule out invalid temperature ranges.

Using Translated Smart Glossaries

The Smart Glossaries described in the previous section (with the exception of DLS_Units_of_Measure_Retail) have been translated for use in various locales. The translated Smart Glossaries are delivered in the following languages:

Brazilian Portuguese

Chinese (Simplified)

French

German

Italian

Japanese

Korean

Spanish

Any or all of these translated Smart Glossaries can be automatically deployed for use on your Oracle DataLens Server as follows:

1. On your Oracle DataLens Administration Server system, log in using the administrator user you established when installing the server, so that you have the correct permissions to copy files.

Note:
This step does not refer to the Oracle DataLens Administration Server Web page.

2. Change directories to the MW_HOME\edqp_template1\autodeploy\translatedsmartglossaries. By default, these directories are:
On Linux and UNIX: /opt/Oracle/Middleware/edqp_template1/autodeploy/ translatedsmartglossaries

On Windows: C:\Oracle\Middleware\edqp_template1\autodeploy\ translatedsmartglossaries

Note:
The preceding directories are the defaults when installing your Oracle DataLens Server; your installation directory may vary.

3. Unzip one or more of the translated Smart Glossary zip files into the same directory.

4. Copy all of the .project files into the MW_HOME\user_projects\domains\dls_domain\opdq\autodeploy\lens directory to configure them for deployment.
This automatically deploys (autodeploys) the DSA and data lens template files to your Oracle DataLens Server. The Oracle DataLens Administration Server polls the local directory every 10 minutes and attempts to autodeploy the DSA and data lens template files that are placed in the respective folders under the autodeploy parent folder.

5. Log out of the Oracle DataLens Administration Server system.

6. Wait 10 minutes to ensure that the server templates have been autodeployed.

7. Log into the Oracle DataLens Server Administration Web page.
For more information about these Web pages, see Oracle Enterprise Data Quality for Product Data Oracle DataLens Server Administration Guide.

8. Verify that all of the .project data lenses that you configured for deployment have been autodeployed.
All of the translated Smart Glossaries can be imported into your various data lenses so that your translated data recognition is enhanced.

A Regular Expressions

Regular Expressions use character pattern matching to find and capture the information you need. Regular Expressions are used most frequently in the Knowledge Studio when creating Terminology rules.

To use Regular Expressions, you must learn the syntax. Regular Expressions use special characters, wildcards, to match a range of other characters. A Regular Expression found in a Terminology rule is surrounded by forward slashes.

Special Characters in Regular Expressions

The following table lists of many of the special characters used in a regular expression and some example expressions:

Wildcard or Meta-Characters

Description and Examples

.

The dot character matches any single character.
For example, the terminology rule regular expression, "/a.b/", matches all text where there is an "a" followed by any single character, followed by a "b", as in, "a5b".

*

The asterisk matches the preceding pattern or character zero or more times.
For example, "/fo*/" matches the following text fragments:

"f", "fo", "foo", "fooo"

Combining the period and asterisk, "/a.*b/" will match "a5b", "a55b", "a123b", and so on.

+

The plus sign matches the preceding pattern or character one or more times.
For example, /ca+r/ matches the following text fragments: "car", "caar" and "caaar", but will not match "cr".

?

The question mark character matches the preceding pattern or character zero or once.
For example, "/ca?r/" matches both "car" and "cr"; it will not match "caar".

{n}

The curly brackets are used to match exactly n instances of the proceeding character or pattern.
For example, "/x{2}/" matches "xx".

Note: The curly brackets are used in the application to differentiate white space bounded text or characters from text or characters that are embedded among other characters with no identifiable white space.

{n,m}

This form of the curly brackets is used to match the preceding character or pattern from n to m times, with n greater than m. If m is not present then the pattern is matched n or more times.
For example, "/x{2,3}/" matches "xx" and "xxx".

[…]

The square brackets match any one of characters inside the brackets. A range of characters in the alphabet can be matched using the hyphen.
For example, "/[xyz]/ "will match any of "x", "y", or "z". Also, "/[xyz]+/" will match "x", "xx", "y", "yy", and so on.

Within square brackets, a range of characters can be defined using the dash (-). For example, "[a-z]" matches any lowercase letter, and "[A-Z]" matches any uppercase letter. When using the dash to define a range of characters, the first character must precede the second character in alphabetic or numeric order.

For example, "[0-9]" is valid, but "[9-0]" is not valid.

(…)

The parentheses are used to group characters.
For example, "(cars?)|bus" will match "car", "cars", or "bus".

Note: The parentheses are equivalent to "(?:…)"

x|y

The pipe (|) character matches either "x" or "y", where "x" or "y" are blocks of characters.
For example, "car|bus" will match either "car" or "bus".

\

Backslash has two meanings:
Matches against characters that normally have special meaning such as star (*) and dot (.), see preceding descriptions. In this case a "\*" matches the star character. Similarly "" matches the dot character.

Used to define a meta-character. The character "w" will normally match "w". A "\w" will match a sequence of alphanumeric characters not interrupted by white space, see the following description.

\w

Matches any alphanumeric character or the underscore. This is identical to "[A-Za-z0-9_]".

\W

Matches any character that is not alphanumeric and not underscore.

\d

Matches all digits. Identical to "[0-9]".
For example, "/\d+/" will match one or more digits.

For example, positive integers.

\D

Matches all non-digits including white space.

\s

Matches any white space character including a tab or a space.

\S

Matches any character other than white space characters.

(?i)

The "(?i)" meta-characters indicate that the following pattern should ignore the case of letters when performing the match.
For example, the pattern "(?i)car" will match "Car", "car", "cAR", and so on. And "(?i)cars?" will match "Car", "Cars", "CarS", and so on.

Note: The syntax differences between this match rule and the following three are where the pattern is inside the parentheses.

(?!pattern1)pattern2

The "(?!…)… meta-characters say that if the first pattern is not present, pattern1, then accept the second pattern, pattern2.
For example, /(?!x)car/ matches "car"; it will not match "xcar".

Note: Both pattern1 and pattern2 are required.

Useful Regular Expressions in Terminology Rules

Year

Zip Code

First Name

Street Name

For more information on regular expressions, see Perl for Dummies, by Paul Hoffman, or Mastering Regular Expressions, by Jeffrey Friedl.

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