Learning of OWL Class Descriptions on Very Large Knowledge Bases

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type article
title Learning of OWL Class Descriptions on Very Large Knowledge Bases
author Sebastian Hellmann, Jens Lehmann, Sören Auer
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The vision of the Semantic Web is to make use of semantic representations on the largest possible scale - the Web. Large knowledge bases such as DBpedia, OpenCyc, GovTrack, and others are emerging and are freely available as Linked Data and SPARQL endpoints. Exploring and analysing such knowledge bases is a significant hurdle for Semantic Web research and practice. As one possible direction for tackling this problem, we present an approach for obtaining complex class descriptions from objects in knowledge bases by using Machine Learning techniques.

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