2008 OntologyLearningAndPopulation

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Subject Headings: Ontology Learning from Text.

Notes

Cited By

Quotes

Book Overview

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.

Foreword

... This book describes the state-of-the-art in computer-based ontology construction and evaluation. …

Part I. Extracting Terms and Synonyms

  • Marko Brunzel. (2008). “The XTREEM Methods for Ontology Learning from Web Documents"

Part II. Taxonomy and Concept Learning

  • Massimo Poesio, Abdulrahman Almuhareb. (2008). “Extracting concept descriptions from the Web: the importance of attributes and values"
  • Johanna Völker, Peter Haase, Pascal Hitzler. (2008). “Learning Expressive Ontologies"
  • Roberto Navigli,]]Paola Velardi]]. (2008). “From Glossaries to Ontologies: Extracting Semantic Structure from Textual Definitions"

Part III. Learning Relations

Part IV. Ontology Population

Part V. Methodology

  • Nathalie Aussenac-Gilles, Sylvie Despres, Sylvie Szulman. (2008). “The TERMINAE Method and Platform for Ontology Engineering from Texts"
  • Elena Simperl, Christoph Tempich, Denny Vrandečić. (2008). “A Methodology for Ontology Learning"

Part VI. Evaluation

  • Klaas Dellschaft, Steffen Staab. (2008). “Strategies for the Evaluation of Ontology Learning",


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2008 OntologyLearningAndPopulationPaul Buitelaar
Philipp Cimiano
Ontology Learning and Population: Bridging the gap between text and knowledgehttp://books.google.com/books?id=wcBSjk2a2 sC2008
AuthorPaul Buitelaar + and Philipp Cimiano +
titleOntology Learning and Population: Bridging the gap between text and knowledge +
titleUrlhttp://books.google.com/books?id=wcBSjk2a2 sC +
year2008 +