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International Workshop on Machine Learning and Data Mining in Pattern Recognition

MLDM 2007: Machine Learning and Data Mining in Pattern Recognition pp 883–898Cite as

Hybrid Learning of Ontology Classes

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4571))

Abstract

Description logics have emerged as one of the most successful formalisms for knowledge representation and reasoning. They are now widely used as a basis for ontologies in the Semantic Web. To extend and analyse ontologies, automated methods for knowledge acquisition and mining are being sought for. Despite its importance for knowledge engineers, the learning problem in description logics has not been investigated as deeply as its counterpart for logic programs.

We propose the novel idea of applying evolutionary inspired methods to solve this task. In particular, we show how Genetic Programming can be applied to the learning problem in description logics and combine it with techniques from Inductive Logic Programming. We base our algorithm on thorough theoretical foundations and present a preliminary evaluation.

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Authors and Affiliations

  1. University of Leipzig, Computer Science Department, Johannisgasse 26, D-04103 Leipzig, Germany

    Jens Lehmann

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  1. Jens Lehmann
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Petra Perner

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© 2007 Springer-Verlag Berlin Heidelberg

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Lehmann, J. (2007). Hybrid Learning of Ontology Classes. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_66

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  • DOI: https://doi.org/10.1007/978-3-540-73499-4_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73498-7

  • Online ISBN: 978-3-540-73499-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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