Overview
- Includes the comparison of different methods in order to provide guidelines for ontology engineers
- Includes an analysis of the impact of ontology learning for certain applications
- Includes supplementary material: sn.pub/extras
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Table of contents (11 chapters)
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Front Matter
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Preliminaries
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Front Matter
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Methods and Applications
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Front Matter
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Conclusion
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Front Matter
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Back Matter
Keywords
About this book
In the last decade, ontologies have received much attention within computer science and related disciplines, most often as the semantic web. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications discusses ontologies for the semantic web, as well as knowledge management, information retrieval, text clustering and classification, as well as natural language processing.
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications is structured for research scientists and practitioners in industry. This book is also suitable for graduate-level students in computer science.
Authors and Affiliations
Bibliographic Information
Book Title: Ontology Learning and Population from Text
Book Subtitle: Algorithms, Evaluation and Applications
Authors: Philipp Cimiano
DOI: https://doi.org/10.1007/978-0-387-39252-3
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag US 2006
Hardcover ISBN: 978-0-387-30632-2Published: 12 October 2006
Softcover ISBN: 978-1-4419-4032-2Published: 29 October 2010
eBook ISBN: 978-0-387-39252-3Published: 11 December 2006
Edition Number: 1
Number of Pages: XXVIII, 347
Topics: Theory of Computation, Information Systems Applications (incl. Internet), Artificial Intelligence, Database Management, Multimedia Information Systems, Computer Communication Networks