Ontology Learning and Population: Bridging the Gap between Text and Knowledge

✍ Scribed by P. Buitelaar, P. Buitelaar, P. Cimiano

• Publisher: IOS Press
• Year: 2008
• Tongue: English
• Leaves: 292
• Series: Frontiers in Artificial Intelligence and Applications 167
• Category: Library

✦ Synopsis

The promise of the Semantic Web is that future web pages will be annotated not only with bright colors and fancy fonts as they are now, but with annotation extracted from large domain ontologies that specify, to a computer in a way that it can exploit, what information is contained on the given web page. The presence of this information will allow software agents to examine pages and to make decisions about content as humans are able to do now. The classic method of building an ontology is to gather a committee of experts in the domain to be modeled by the ontology, and to have this committee agree on which concepts cover the domain, on which terms describe which concepts, on what relations exist between each concept and what the possible attributes of each concept are. All ontology learning systems begin with an ontology structure, which may just be an empty logical structure, and a collection of texts in the domain to be modeled. An ontology learning system can be seen as an interplay between three things: an existing ontology, a collection of texts, and lexical syntactic patterns. The Semantic Web will only be a reality if we can create structured, unambiguous ontologies that model domain knowledge that computers can handle. The creation of vast arrays of such ontologies, to be used to mark-up web pages for the Semantic Web, can only be accomplished by computer tools that can extract and build large parts of these ontologies automatically. This book provides the state-of-art of many automatic extraction and modeling techniques for ontology building. The maturation of these techniques will lead to the creation of the Semantic Web.

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✦ Table of Contents

Title page......Page 1
On the "Ontology" in Ontology Learning......Page 5
Foreword......Page 11
Contents......Page 15
Extracting Terms and Synonyms......Page 17
The XTREEM Methods for Ontology Learning from Web Documents......Page 19
Taxonomy and Concept Learning......Page 43
Extracting Concept Descriptions from the Web: The Importance of Attributes and Values......Page 45
Learning Expressive Ontologies......Page 61
From Glossaries to Ontologies: Extracting Semantic Structure from Textual Definitions......Page 87
Learning Relations......Page 105
Unsupervised Learning of Semantic Relations for Molecular Biology Ontologies......Page 107
Ontology Population......Page 121
NLP Techniques for Term Extraction and Ontology Population......Page 123
Weakly Supervised Approaches for Ontology Population......Page 145
Information Extraction and Semantic Annotation of Wikipedia......Page 161
Automatically Harvesting and Ontologizing Semantic Relations......Page 187
Methodology......Page 213
The TERMINAE Method and Platform for Ontology Engineering from Texts......Page 215
A Methodology for Ontology Learning......Page 241
Evaluation......Page 267
Strategies for the Evaluation of Ontology Learning......Page 269
Author Index......Page 289