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Testing OWL Axioms against RDF Facts: A Possibilistic Approach

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Knowledge Engineering and Knowledge Management (EKAW 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8876))

Abstract

Automatic knowledge base enrichment methods rely critically on candidate axiom scoring. The most popular scoring heuristics proposed in the literature are based on statistical inference. We argue that such a probability-based framework is not always completely satisfactory and propose a novel, alternative scoring heuristics expressed in terms of possibility theory, whereby a candidate axiom receives a bipolar score consisting of a degree of possibility and a degree of necessity. We evaluate our proposal by applying it to the problem of testing SubClassOf axioms against the DBpedia RDF dataset.

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Author information

Authors and Affiliations

  1. Univ. Nice Sophia Antipolis, I3S, UMR 7271, Sophia Antipolis, France

    Andrea G. B. Tettamanzi & Catherine Faron-Zucker

  2. INRIA, Sophia Antipolis, France

    Fabien Gandon

Authors
  1. Andrea G. B. Tettamanzi
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  2. Catherine Faron-Zucker
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  3. Fabien Gandon
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Editor information

Editors and Affiliations

  1. Department of Geography, University of California, Santa Barbara, CA, USA

    Krzysztof Janowicz

  2. Dept. of Computer Science, VU University Amsterdam, The Netherlands

    Stefan Schlobach

  3. University of Linköping, Sweden

    Patrick Lambrix

  4. Aalto University, Finland

    Eero Hyvönen

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© 2014 Springer International Publishing Switzerland

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Tettamanzi, A.G.B., Faron-Zucker, C., Gandon, F. (2014). Testing OWL Axioms against RDF Facts: A Possibilistic Approach. In: Janowicz, K., Schlobach, S., Lambrix, P., Hyvönen, E. (eds) Knowledge Engineering and Knowledge Management. EKAW 2014. Lecture Notes in Computer Science(), vol 8876. Springer, Cham. https://doi.org/10.1007/978-3-319-13704-9_39

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  • DOI: https://doi.org/10.1007/978-3-319-13704-9_39

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13703-2

  • Online ISBN: 978-3-319-13704-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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