Ontology Learning for the Semantic Web

By: Alexander Maedche

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Hardcover | 28 February 2002

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276 Pages

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Recently, the vision of a semantic web in which background knowledge is stored through the use of machine-processable metadata has been attained. Ontologies have shown to be the answer for structuring background knowledge by providing a formal and complex vocabulary of a particular domain that is shared by a group of people. The manual building of ontologies is a tedious task, which can easily result in a knowledge acquisition bottleneck. However, the success of the semantic web depends on the proliferation of ontologies, which requires that the engineering of ontologies be completed quickly and easily. This book explores techniques for applying knowledge discovery techniques to different web data sources (such as HTML documents, dictionaries, etc.), in order to support the task of engineering and maintaining ontologies. The approach of ontology learning proposed in this book includes a number of complementary disciplines that feed in different types of unstructured and semi-structured data. This data is necessary in order to support a semi-automatic ontology engineering process. This text is designed for researchers and developers of semantic web applications. It could also serve as a supplemental reference to advanced level courses in ontologies and the semantic web.

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You Can Find This Book In

Non-Fiction Philosophy Metaphysics & Ontology Computing & I.T.Graphical & Digital Media Applications Web Graphics & Design Reference, Information & Interdisciplinary Subjects Encyclopaedias & Reference Works Medicine Medicine in General Digital Lifestyle & Online World: Consumer & User Guides Internet Guides & Online Services

Computer Science Artificial Intelligence Machine Learning Computer Programming & Software Development Databases

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Paperback

Published: 31st October 2012

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List of Figures	p. ix
List of Tables	p. xiii
Preface	p. xv
Acknowledgements	p. xviii
Foreword	p. xix
Fundamentals
Introduction	p. 3
Motivation and Problem Description	p. 3
Research Questions	p. 4
Reader's Guide	p. 6
Ontology--Definition and Overview	p. 11
Ontologies for Communication--A Layered Approach	p. 15
Development and Application of Ontologies	p. 21
Conclusion	p. 25
Layered Ontology Engineering	p. 29
Ontology Engineering Framework	p. 30
Layered Representation	p. 34
Conclusion	p. 49
Further Topics in Ontology Engineering	p. 50
Ontology Learning for Ontology Engineering	p. 51
Ontology Learning for the Semantic Web
Ontology Learning Framework	p. 59
A Taxonomy of Relevant Data for Ontology Learning	p. 60
An Architecture for Ontology Learning	p. 66
Overview of the Architecture Components	p. 66
Ontology Engineering Workbench ONTOEDIT	p. 68
Data Import and Processing Component	p. 70
Algorithm Library	p. 71
Graphical User Interface and Management Component	p. 72
Phases of Ontology Learning	p. 73
Import and Reuse	p. 74
Extract	p. 75
Prune	p. 76
Refine	p. 77
Conclusion	p. 78
Data Import and Processing	p. 81
Importing and Processing Existing Ontologies	p. 83
Ontology Wrapper and Import	p. 84
FCA-MERGE--Bottom-Up Ontology Merging	p. 85
Collecting, Importing and Processing Documents	p. 95
Ontology-focused Document Crawling	p. 95
Shallow Text Processing using SMES	p. 97
Semi-Structured Document Wrapper	p. 105
Transforming Data into Relational Structures	p. 107
Conclusion	p. 112
Language Processing for Ontology Learning	p. 112
Ontology Learning from Web Documents	p. 113
(Multi-)Relational Data	p. 114
Ontology Learning Algorithms	p. 117
Algorithms for Ontology Extraction	p. 118
Lexical Entry Extraction	p. 118
Taxonomy Extraction	p. 122
Non-Taxonomic Relation Extraction	p. 130
Algorithms for Ontology Maintenance	p. 140
Ontology Pruning	p. 140
Ontology Refinement	p. 142
Conclusion	p. 144
Multi-Strategy Learning	p. 145
Taxonomic vs. Non-Taxonomic Relations	p. 145
A Note on Learning Axioms--A[superscript o]	p. 146
Implementation and Evaluation
The Text-to-onto Environment	p. 151
Component-based Architecture	p. 153
Ontology Engineering Environment OntoEdit	p. 154
Components for Ontology Learning	p. 163
Conclusion	p. 168
Evaluation	p. 171
The Evaluation Approach	p. 172
Ontology Comparison Measures	p. 173
Precision and Recall	p. 174
Lexical Comparison Level Measures	p. 175
Conceptual Comparison Level Measures	p. 177
Human Performance Evaluation	p. 183
Ontology Engineering Evaluation Study	p. 184
Human Evaluation--Precision and Recall	p. 185
Human Evaluation--Lexical Comparison Level	p. 187
Human Evaluation--Conceptual Comparison Level	p. 188
Ontology Learning Performance Evaluation	p. 190
The Evaluation Setting	p. 191
Evaluation of Lexical Entry Extraction	p. 191
Evaluation of Concept Hierarchy Extraction	p. 193
Evaluation of Non-Taxonomic Relation Extraction	p. 194
Conclusion	p. 196
Application-oriented Evaluation	p. 197
Standard Datasets for Evaluation	p. 198
Related Work and Outlook
Related Work	p. 203
Related Work on Ontology Engineering	p. 204
Related Work on Frameworks of KA and ML	p. 209
Related Work on Data Import and Processing	p. 212
Related Work on Algorithms	p. 214
Related Work on Evaluation	p. 219
Conclusion and Outlook	p. 223
Contributions	p. 223
Insights into Ontology Learning	p. 224
Unanswered Questions	p. 225
Future Research	p. 226
References	p. 228
Index	p. 242
Table of Contents provided by Syndetics. All Rights Reserved.

Ontology Learning for the Semantic Web

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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