Ontology Learning from Text: Methods, Evaluation and Applications (Frontiers in Artificial Intelligence and Applications, Vol. 123)

P. Buitelaar, P. Cimiano, B. Magnini

ISBN 10: 1586035231 / ISBN 13: 9781586035235

Editore: IOS Press, 2005

Riguardo questo articolo

Like New. Codice articolo ERICA79715860352316

Dati bibliografici

Titolo: Ontology Learning from Text: Methods, ...

Casa editrice: IOS Press

Data di pubblicazione: 2005

Legatura: Hardcover

Condizione: Like New

Tipologia articolo: book

Descrizione articolo

Riassunto:

Buitelaar (German Center for Artificial Intelligence, Germany) et al. compile 10 essays from two workshops on ontology learning and knowledge acquisition from the European Conference on Artificial Intelligence 2004 and the International Conference on Knowledge Engineering and Management 2004. The three sections emphasize methodologies (information theory, clustering, labeling, and learning taxonomic relations), evaluation (lexical aspects, OntoLearn, and tasked-based frameworks), and applications (web service descriptions, knowledge management, and information extraction). Contributors are internationally based. Only an author index is provided. Annotation ©2006 Book News, Inc., Portland, OR (booknews.com)

Dal risvolto di copertina interno:

"This volume provides an excellent snapshot of the current state of the art in ontology learning and the related issue of ontology evaluation. It will be of interest not just to researchers involved in ontologies but also to the wider CL and NLP community who are interested to see where their component technologies are being used."- Christopher Brewster, Computational Linguistics

This volume brings together ontology learning, knowledge acquisition and other related topics. It presents current research in ontology learning, addressing three perspectives. The first perspective looks at methodologies that have been proposed to automatically extract information from texts and to give a structured organization to such knowledge, including approaches based on machine learning techniques. Then there are evaluation methods for ontology learning, aiming at defining procedures and metrics for a quantitative evaluation of the ontology learning task; and finally application scenarios that make ontology learning a challenging area in the context of real applications such as bio-informatics. According to the three perspectives mentioned above, the book is divided into three sections, each including a selection of papers addressing respectively the methods, the applications and the evaluation of ontology learning approaches.