Intelligent Systems Reference Library

A Compendium

By: Edward H. Y. Lim, James N. K. Liu, Raymond S. T. Lee

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Hardcover | 19 January 2011

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Hardcover
264 Pages

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23.5 x 15.88 x 1.91

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The Knowledge Seeker is a useful system to develop various intelligent applications such as ontology-based search engine, ontology-based text classification system, ontological agent system, and semantic web system etc. The Knowledge Seeker contains four different ontological components. First, it defines the knowledge representation model ¡V Ontology Graph. Second, an ontology learning process that based on chi-square statistics is proposed for automatic learning an Ontology Graph from texts for different domains. Third, it defines an ontology generation method that transforms the learning outcome to the Ontology Graph format for machine processing and also can be visualized for human validation. Fourth, it defines different ontological operations (such as similarity measurement and text classification) that can be carried out with the use of generated Ontology Graphs. The final goal of the KnowledgeSeeker system framework is that it can improve the traditional information system with higher efficiency. In particular, it can increase the accuracy of a text classification system, and also enhance the search intelligence in a search engine. This can be done by enhancing the system with machine processable ontology.

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You Can Find This Book In

Non-Fiction Philosophy Metaphysics & Ontology Computing & I.T.Databases Information Retrieval Computer Science Mathematical Theory of Computation Business & Management Management & Management Techniques Management of Specific Areas Knowledge Management

Artificial Intelligence Expert Systems & Knowledge-Based Systems Operational Research Management Decision Making

Introduction
Computational Knowledge and Ontology	p. 3
What Is Knowledge?	p. 3
Computational Knowledge in Information Systems	p. 3
Knowledge Engineering	p. 4
Knowledge Representation	p. 4
What Is Ontology?	p. 6
Ontology Modeling in Computer Systems	p. 7
Computational Ontology Representation	p. 7
Top-Level Ontologies	p. 8
Lexical Ontologies	p. 9
Domain Ontologies	p. 11
Ontology Engineering	p. 13
Introduction	p. 13
Ontology Fundamentals	p. 14
Ontological Structure	p. 14
Ontological Taxonomy	p. 15
Ontological Relations	p. 16
Lexical Relations	p. 18
Ontology Engineering Tools	p. 19
Protégé ontology editor	p. 20
Ontology Learning from Text	p. 21
Learning Terminology	p. 23
Learning Taxonomy	p. 25
Text Information Retrieval	p. 27
Information Retrieval Model	p. 27
Term Weighting Model	p. 27
Text Classification	p. 28
Text Clustering	p. 31
Feature Selection and Reduction	p. 32
Search Engine Modeling	p. 34
Evaluation Methods	p. 35
Performance measurement	p. 36
Web Data Semantics	p. 37
Semantic Web	p. 37
W3C Semantic Web	p. 39
Semantic Modeling	p. 40
Taxonomy	p. 41
Thesaurus	p. 41
Topic Maps	p. 42
Ontology	p. 43
Ontology Languages for the Semantic Web	p. 44
Semantic Annotation and Information Retrieval	p. 46
KnowledgeSeeker: An Ontology Modeling and Learning Framework
Ontology Modeling Framework	p. 49
KnowledgeSeeker - The System Overviews	p. 49
Background of Signs System and Ontology	p. 51
The Semiotics	p. 51
The Sign System for Concept Formation	p. 54
The Meaning of Words	p. 56
The Semantics of Relations	p. 57
Ontology Graph Overviews	p. 59
Nodes in Ontology Graph	p. 60
Term Nodes in Ontology Graph	p. 62
Words Function	p. 64
The Function of Language	p. 64
Understanding and Feeling	p. 65
Meaning and Information	p. 67
The Implementation of Ontology Graph	p. 68
The Conceptual Structure of Ontology Graph	p. 68
Conceptual Units in Ontology Graph	p. 69
The Class Diagram of Ontology Graph	p. 69
Ontology Learning in Chinese Text	p. 71
The Ontology Learning Method	p. 71
Term Extraction	p. 72
Term-to-Class Relationship Mapping	p. 73
Term-to-Class Independency Measurement by X²	p. 73
Term-to-Class Positive and Negative Dependency Measurement by R	p. 78
Term-to-Term Relationship Mapping	p. 81
Term-to-Term Independency Measurement by X²	p. 83
Concept Clustering	p. 91
Sample Result of Domain Ontology Graph Generation (10 Domains)	p. 96
Ontology Graph Generation Process	p. 99
Ontology in Information Systems	p. 99
Ontology Graph Generation Process in KnowledgeSeeker	p. 100
Definition of Ontology Graph Structure	p. 101
Domain Ontology Graph Generation Process	p. 103
Document Ontology Graph Generation	p. 105
Automatic Generation of Domain Ontology Graph	p. 107
Experimental Setup	p. 108
Experimental Results	p. 110
Ontology Graph Operations	p. 121
Ontology Graph Matching and Querying Process	p. 121
Introduction to Ontology Matching and Mapping	p. 121
Ontology Graph Matching Methods	p. 122
Semantic Mapping Function	p. 122
Similarity Measurement Function	p. 123
Matching Different Components with Ontology Graph	p. 124
Matching Terms to Domain Ontology Graph	p. 125
Matching Text Document to Domain Ontology Graph	p. 128
Ontology Graph Based Similarity Measurement	p. 132
Matching Two Document Ontology Graphs	p. 134
Overviews of Ontology Graph Based Querying	p. 137
Ontology Graph Querying Methods	p. 137
Operations in Ontology Graph Based Querying	p. 137
Querying Document with Document Ontology Graph	p. 138
KnowledgeSeeker Applications
Ontology Graph Based Approach for Automatic Chinese Text Classification	p. 145
Introduction	p. 145
Methodologies	p. 147
Ontology Graphs Reviews	p. 147
Classification Algorithm	p. 148
Experiments	p. 149
Experiment Description	p. 150
Evaluate the Performance of Ontology-Graph Based Approach (Experiment 1)	p. 150
Evaluate the Optimal Size of Domain Ontology Graph for Best Classification Result (Experiment 2)	p. 151
Evaluate the Effects for Setting Different Thresholds of weight of Domain Ontology Graph (Experiment 3)	p. 151
Evaluation Method	p. 151
Performance on Ontology Graph Based Text Classification	p. 152
Experimental Results	p. 153
Performance on Ontology Graph Based Text Classification (Experiment 1)	p. 153
Performance on Using Different Size of Terms (Dimensionality) for Text Classification (Experiment 2)	p. 156
Result of Using Different Thresholds of Weight of Domain Ontology Graphs (Experiment 3)	p. 161
Combining the Results and Optimizing the Parameters for the Text Classification Experiments	p. 163
IATOPIA iCMS KnowledgeSeeker - An Integrated Content Management System and Digital Asset Management System (DAMS)	p. 165
IATOPIA iCMS KnowledgeSeeker	p. 165
System Features	p. 165
System Model and Architecture	p. 166
Ontology System	p. 168
Search Indexing System	p. 168
IATOPIA Search Engine	p. 169
IATOPIA Digital Asset Management System (DAMS)	p. 170
DAMS System Architecture Overview	p. 170
IATOIPA iCMS Databank Cluster in DAMS	p. 170
Ontology System in DAMS	p. 171
DAMS and Web Channel Interface Examples	p. 172
IATOPIA News Channel (IAToNews) - An Intelligent Ontological Agent-Based Web News Retrieval and Search System	p. 175
Introduction	p. 175
lAToNews System Architecture Overview	p. 175
Ontology System in IAToNews	p. 176
Article Ontology	p. 177
Topic Ontology	p. 178
Ontology Based Content Indexing	p. 178
lAToNews Web Interface Examples	p. 179
Collaborative Content and User-Based Web Ontology Learning System	p. 181
Introduction	p. 181
Background	p. 182
Problem of Building a Generalized Ontology to Satisfy Daily Life	p. 182
Semantic Web	p. 182
Web Channels	p. 182
BuBo (Feedback, Personalization with User Ontology	p. 183
Methodology	p. 133
Overview of System Architecture	p. 183
Content-Based Ontology Learning Process	p. 184
User-Based Ontology Personalization Process	p. 187
Implementation	p. 192
Architecture of the Collaborative System Implementation	p. 192
Structure of the Specified Domains Ontology with Generalized OG	p. 193
Ontology-Based Search Engine Within BuBo	p. 194
Conclusions	p. 194
References	p. 195
Appendix	p. 211
Table of Contents provided by Ingram. All Rights Reserved.

Intelligent Systems Reference Library

A Compendium

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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