Using iKnow
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Preface : 
 
 
Chapter 1: 
  1.1 A Simple Use Case
1.2 What is iKnow?
1.2.1 What iKnow Isn’t
1.3 Logical Text Units Identified by iKnow
1.3.1 Sentences
1.3.2 Entities
1.3.3 CRCs and CCs
1.3.4 Paths
1.4 Smart Indexing
1.5 Smart Matching
Chapter 2: 
  2.1 A Note on Program Examples
2.2 A Note on %Persistent Object Methods
2.3 A Note on %iKnow and %SYSTEM.iKnow
2.4 Space Requirements and iKnow Globals
2.4.1 Batch Load Space Allocation
2.5 Input Data
2.5.1 File Formats
2.5.2 SQL Record Format
2.5.3 Text Normalization
2.5.4 User-defined Source Normalization
2.6 Output Structures
2.7 Constants
2.8 Error Codes
Chapter 3: 
  3.1 Accessing iKnow Architect
3.1.1 Enabling a Namespace
3.2 Creating a Domain
3.2.1 Opening a Domain
3.2.2 Changing the Domain Name and Check Boxes
3.2.3 Deleting a Domain
3.3 Model Elements
3.3.1 Domain Settings
3.3.2 Metadata Fields
3.3.3 Data Locations
3.3.4 Blacklists
3.3.5 Matching
3.4 Save, Compile, and Build
3.5 Knowledge Portal
3.5.1 Selecting a Domain
3.5.2 Listing All Concepts
3.5.3 Analyzing a Specified Entity
3.5.4 Limiting the Sources to Analyze
3.6 Indexing Results
3.6.1 Indexed Sentences
3.6.2 Concepts and CRCs
Chapter 4: 
  4.1 Swagger
4.1.1 Returning iKnow Data Using Swagger
4.2 REST Operations
4.2.1 Domains and Configurations
4.2.2 Sources
4.2.3 Entities
4.2.4 Sentences
4.2.5 Paths and CRCs
4.2.6 Dictionaries and Matching
4.2.7 Blacklists
Chapter 5: 
  5.1 iKnow Domains
5.1.1 Defining a Domain as a Subclass
5.1.2 Defining a Domain Programmatically
5.1.3 Setting Domain Parameters
5.1.4 Assigning to a Domain
5.1.5 Deleting All Data from a Domain
5.1.6 Listing All Domains
5.1.7 Renaming a Domain
5.1.8 Copying a Domain
5.2 iKnow Configurations
5.2.1 Defining a Configuration
5.2.2 Setting Configuration Properties
5.2.3 Using a Configuration
5.2.4 Listing All Configurations
5.2.5 Using a Configuration to Normalize a String
5.3 iKnow UserDictionary
5.3.1 UserDictionary Format
5.3.2 Defining a UserDictionary as an Object Instance
5.3.3 Defining a UserDictionary as a File
Chapter 6: 
  6.1 Loader
6.1.1 Loader Error Logging
6.1.2 Loader Reset()
6.2 Lister
6.2.1 Initializing a Lister
6.2.2 Overriding Lister Instance Defaults
6.2.3 Lister Assigns IDs to Sources
6.2.4 Lister Defaults Example
6.2.5 Lister Parameters
6.2.6 Batch or List?
6.3 Listing and Loading Examples
6.3.1 Loading Files
6.3.2 Loading SQL Records
6.3.3 Loading Elements of a Subscripted Global
6.3.4 Loading a String
6.4 Updating the Domain Contents
6.4.1 Adding Sources
6.4.2 Deleting Sources
6.5 Loading a Virtual Source
6.5.1 Deleting a Virtual Source
6.6 Copying and Re-indexing Loaded Source Data
6.6.1 UserDictionary and Copied Sources
Chapter 7: 
 
Chapter 8: 
  8.1 Types of Queries
8.2 Queries Described in this Chapter
8.3 Query Method Parameters
8.4 Counting Sources and Sentences
8.5 Counting Entities
8.6 Listing Top Entities
8.6.1 GetTop(): Most-Frequently-Occurring Entities
8.6.2 GetTopTFIDF() and GetTopBM25()
8.7 CRC Queries
8.7.1 Listing CRCs that Contain Entities
8.7.2 Counting Sources that Contain a CRC
8.7.3 Listing Sources or Sentences that Fulfill a CRC Mask
8.8 Listing Similar Entities
8.8.1 Parts and N-grams
8.9 Listing Related Entities
8.9.1 Limiting by Position
8.10 Counting Paths
8.11 Listing Similar Sources
8.12 Summarizing a Source
8.12.1 Custom Summaries
8.13 Querying a Subset of the Sources
Chapter 9: 
  9.1 Negation
9.1.1 Properties of Formal Negation
9.1.2 Using Negation Attributes
9.1.3 Negation Attribute Structure
9.1.4 Negation Bit Map
9.1.5 Negation and Dictionary Matching
9.1.6 Negation Examples
9.1.7 Adding Negation Terms
9.1.8 Negation Special Cases
9.2 Sentiment
9.2.1 Using Sentiment Attributes
Chapter 10: 
  10.1 Configuring Stemming
10.1.1 Hunspell
10.2 Stem Retrieval Methods
10.3 Using Stems
Chapter 11: 
  11.1 Creating a Blacklist
11.1.1 Blacklists and Domains
11.2 Queries that Support Blacklists
11.2.1 Blacklist Query Example
Chapter 12: 
  12.1 Supported Filters
12.2 Filtering by the ID of the Source
12.2.1 By External Id
12.2.2 By Source Id
12.3 Filtering a Random Selection of Sources
12.4 Filtering by Number of Sentences
12.5 Filtering by Entity Match
12.5.1 Filtering by Dictionary Match
12.6 Filtering by Indexing Date Metadata
12.7 Filtering by User-defined Metadata
12.7.1 Metadata Filter Operators
12.8 Filtering by SQL Query
12.9 Filter Modes
12.10 Using GroupFilter to Combine Multiple Filters
Chapter 13: 
  13.1 Text Categorization Implementation
13.1.1 Implementation Interfaces
13.2 Establishing a Training Set and a Test Set
13.3 Building a Text Classifier Programmatically
13.3.1 Create a Text Classifier
13.3.2 Populate the Terms Dictionary
13.3.3 Run the Classification Optimizer
13.3.4 Generate the Text Classifier
13.4 Testing a Text Classifier
13.4.1 Using Test Results
13.5 Building a Text Classifier Using the UI
13.5.1 Define a Data Set for the UI
13.5.2 Build a Text Classifier
13.5.3 Optimize the Text Classifier
13.5.4 Test the Text Classifier against a Test Set of Data
13.5.5 Test the Text Classifier on Uncategorized Data
13.6 Using a Text Classifier
Chapter 14: 
  14.1 Semantic Dominance
14.1.1 Dominance in Context
14.1.2 Concepts of Semantic Dominance
14.1.3 Semantic Dominance Examples
14.2 Semantic Proximity
14.2.1 Japanese Semantic Proximity
14.2.2 Proximity Examples
Chapter 15: 
  15.1 Implementing Custom Metrics
15.2 Types and Targets
15.3 Copying Metrics
Chapter 16: 
  16.1 Introducing Dictionary Structure and Matching
16.1.1 Terminology
16.2 Creating a Dictionary
16.2.1 Dictionaries and Domains
16.2.2 Dictionary Creation Examples
16.2.3 Defining a Format Term
16.2.4 Multiple Formats in a Dictionary Term
16.3 Listing and Copying Dictionaries
16.3.1 Listing Existing Dictionaries
16.3.2 Copying Dictionaries
16.4 Extending Dictionary Constructs
Chapter 17: 
  17.1 How Dictionary Matching Works
17.1.1 Match Scoring
17.2 Matching A String
17.2.1 Matching an Entity String
17.2.2 Matching a Sentence String
17.3 Matching Sources
17.4 Defining a Matching Profile
17.4.1 Matching Profile Properties
17.4.2 Domain Default Matching Profile
Chapter 18: 
  18.1 How to Display iKnow User Interfaces
18.2 Abstract Portal
18.3 Abstract Source Viewer
18.4 Loading Wizard
18.5 Knowledge Portal
18.6 Basic Portal
18.7 Indexing Results
18.8 Matching Results
Chapter 19: 
  19.1 iKnow Shell Interface
19.1.1 List, Show, and Summarize Sources
19.1.2 Filter Sources
19.2 iKnow Data Upgrade Utility
19.2.1 Notes on Specific Version Upgrades
Chapter 20: 
  20.1 Available Web Services
20.2 Using an iKnow Web Service
20.3 Example
20.4 Comparison of iKnow Web Services with Primary iKnow APIs
20.5 See Also
Chapter 21: 
  21.1 KPI Terminology
21.2 Defining a KPI That Uses an iKnow Query
21.3 Available KPI Filters
21.4 Overriding the KPI Properties
21.5 Example
21.6 Creating a Dashboard to Display the KPI
21.6.1 Creating Dashboards: Basics
21.6.2 Changing the Series Names
21.6.3 Configuring the Properties
21.6.4 Adding Previous Page and Next Page Buttons
21.6.5 Example Dashboard with iKnow KPI
21.7 Providing Access to Dashboards
21.8 See Also
Chapter 22: 
  22.1 Custom Lister
22.1.1 Lister name
22.1.2 SplitFullRef() and BuildFullRef()
22.1.3 Default Processor
22.1.4 Expand List
22.2 Custom Processor
22.2.1 Metadata
22.3 Custom Converter
22.3.1 %OnNew
22.3.2 Buffer String
22.3.3 Convert
22.3.4 Next Converted Part
Chapter 23: 
  23.1 Configuring Automatic Language Identification
23.2 Using Automatic Language Identification
23.2.1 Language Identification Queries
23.3 Overriding Automatic Language Identification
23.4 Language-Specific Issues
Chapter 24: 
  24.1 Indexing Sources for iFind Search
24.1.1 Indexing a JSON Object
24.2 Performing iFind Search
24.2.1 Validating a search-items String
24.2.2 Fuzzy Search
24.2.3 Stemming and Decompounding
24.2.4 Languages Not Supported by the iKnow Engine
24.3 iFind Examples
24.3.1 Basic Search Examples
24.3.2 Semantic Syntax Examples
 
Appendix A: 
 
Appendix B: 
  B.1 Overview
B.1.1 Background
B.1.2 Relationship between a Cube and iKnow Sources
B.2 Generating iKnow Source Metadata Fields for an Associated DeepSee Cube
B.2.1 Prerequisites
B.2.2 Generating the Metadata Fields from the Cube
B.2.3 Using DeepSee Filters with an iKnow KPI
B.3 Generating DeepSee Cubes for an iKnow Domain
B.3.1 Generating the Cubes
B.3.2 A Brief Look at the Analyzer and the Cubes
B.3.3 Rebuilding the Cubes
B.4 See Also