Learning of OWL Class Descriptions on Very Large Knowledge Bases

Presented at: 7th International Semantic Web Conference (ISWC2008)

by Sebastian Hellmann, Jens Lehmann, Sören Auer

Webpage: http://ceur-ws.org/Vol-401/iswc2008pd_submission_83.pdf
Webpage: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-401/iswc2008pd_submission_83.pdf

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. We describe how we leverage existing techniques to achieve scalability on large knowledge bases available as SPARQL endpoints or Linked Data. Our algorithms are made available in the open source DL-Learner project and can be used in real-life scenarios by Semantic Web applications.

Keywords: Class Description, Large Knowledge Bases, Machine Learning, OWL, SPARQL

Resource URI on the dog food server: http://data.semanticweb.org/conference/iswc/2008/paper/poster_demo/83

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