Class Expression Learning for Ontology Engineering

Jens Lehmann, Sören Auer, Lorenz Bühmann, Sebastian Tramp


While the number of knowledge bases in the Semantic Web increases, the maintenance and creation of ontology schemata still remain a challenge. In particular creating class expressions constitutes one of the more demanding aspects of ontology engineering. In this article we describe how to adapt a semi-automatic method for learning OWL class expressions to the ontology engineering use case. Specifically, we describe how to extend an existing learning algorithm for the class learning problem. We perform rigorous performance optimization of the underlying algorithms for providing instant suggestions to the user. We also present two plugins, which use the algorithm, for the popular Protégé and OntoWiki ontology editors and provide a preliminary evaluation on real ontologies.

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Type of Paper: research paper
Keywords: Ontology Engineering; Supervised Machine Learning; Concept Learning; Ontology Editor Plugins; OWL; Heuristics
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