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Knowledge Seeker - Ontology Modelling for Information Search and Management A Compendium

:	Edward H. Y. Lim, James N. K. Liu, Raymond S.T. Lee
:	Knowledge Seeker - Ontology Modelling for Information Search and Management A Compendium
:	Springer-Verlag
:	9783642179167
:	1
:	CHF 86.00
:

:	Allgemeines, Lexika
:	English

:	237
:	Wasserzeichen
:	PC/MAC/eReader/Tablet
:	PDF

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.

	Title	1
	Preface	5
	Contents	9
	Part I Introduction	23
	Chapter 1 Computational Knowledge and Ontology	24
	What Is Knowledge?	24
	Computational Knowledge in Information Systems	24
	Knowledge Engineering	25
	Knowledge Representation	25
	What Is Ontology?	27
	Ontology Modeling in Computer Systems	28
	Computational Ontology Representation	28
	Top-Level Ontologies	29
	Lexical Ontologies	30
	Domain Ontologies	32
	Chapter 2 Ontology Engineering	34
	Introduction	34
	Ontology Fundamentals	35
	Ontological Structure	35
	Ontological Taxonomy	36
	Ontological Relations	37
	Lexical Relations	39
	Ontology Engineering Tools	40
	Protégé Ontology Editor	41
	Ontology Learning from Text	42
	Learning Terminology	44
	Learning Taxonomy	46
	Chapter 3 Text Information Retrieval	47
	Information Retrieval Model	47
	Term Weighting Model	47
	Text Classification	48
	Text Clustering	51
	Feature Selection and Reduction	52
	Search Engine Modeling	54
	Evaluation Methods	55
	Performance Measurement	56
	Chapter 4 Web Data Semantics	57
	Semantic Web	57
	W3C Semantic Web	59
	Semantic Modeling	60
	Taxonomy	61
	Thesaurus	61
	Topic Maps	62
	Ontology	63
	Ontology Languages for the Semantic Web	64
	Semantic Annotation and Information Retrieval	66
	Part II KnowledgeSeeker: An Ontology Modeling and Learning Framework	67
	Chapter 5 Ontology Modeling Framework	68
	KnowledgeSeeker The System Overviews	68
	Background of Signs System and Ontology	70
	The Semiotics	70
	The Sign System for Concept Formation	73
	The Meaning of Words	75
	The Semantics of Relations	76
	Ontology Graph Overviews	78
	Nodes in Ontology Graph	79
	Term Nodes in Ontology Graph	81
	Words Function	83
	The Implementation of Ontology Graph	87
	The Conceptual Structure of Ontology Graph	87
	The Class Diagram of Ontology Graph<