Learning OWL Class Expressions

Learning OWL Class Expressions

eBook - 2010
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In this thesis, Lehmann (University of Leipzig) shows how class expressions in OWL, the popular language for modeling ontologies on the web, can be used to leverage machine-learning approaches for solving classification problems in knowledge acquisition on the semantic web. The thesis applies methods from inductive logic programming (ILP) to learning in description logic knowledge bases. Results provide foundations for the semi-automatic creation and maintenance of OWL ontologies, in particular in cases where information is missing, as often happens when extracting knowledge from different sources. Three appendices are included, along with a few color screenshots. There is no subject index. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com)

Blackwell Publishing
With the advent of the Semantic Web and Semantic Technologies, ontologies have become one of the most prominent paradigms for knowledge representation and reasoning. However, recent progress in the field faces a lack of well structured ontologies with large amounts of instance data due to the fact that engineering such ontologies requires a considerable investment of resources. Nowadays, knowledge bases often provide large volumes of data without sophisticated schemata. Hence, methods for automated schema acquisition and maintenance are sought. Schema acquisition is closely related to solving typical classification problems in machine learning, e.g. the detection of chemical compounds causing cancer. In this work, we investigate both, the underlying machine learning techniques and their application to knowledge acquisition in the Semantic Web.

Publisher: Heidelberg :, AKA Akademische Verlagesellschaft :, IOS Press,, 2010
ISBN: 9781614993407
Characteristics: 1 online resource (xiv, 265 pages) : illustrations (some color)


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