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Inductive Learning of Disjointness Axioms

  • Conference paper
On the Move to Meaningful Internet Systems: OTM 2011 (OTM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7045))

Abstract

The tremendous amounts of linked data available on the web are a valuable resource for a variety of semantic applications. However, these applications often need to face the challenges posed by flawed or underspecified representations. The sheer size of these data sets, being one of their most appealing features, is at the same time a hurdle on the way towards more accurate data because this size and the dynamics of the data often hinder manual maintenance and quality assurance. Schemas or ontologies constraining, e.g., the possible instantiations of classes and properties, could facilitate the automated detection of undesired usage patterns or incorrect assertions, but only few knowledge repositories feature schema-level knowledge of sufficient expressivity. In this paper, we present several approaches to enriching learned or manually engineered ontologies with disjointness axioms, an important prerequisite for the applicability of logical approaches to knowledge base debugging. We describe the strengths and weaknesses of these approaches and report on a detailed evaluation based on the DBpedia dataset.

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Author information

Authors and Affiliations

  1. KR & KM Research Group, University of Mannheim, Germany

    Daniel Fleischhacker & Johanna Völker

Authors
  1. Daniel Fleischhacker
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  2. Johanna Völker
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Editor information

Editors and Affiliations

  1. STAR Lab, Vrije Universiteit Brussel (VUB), Bldg G/10, Pleinlaan 2, 1050, Brussel, Belgium

    Robert Meersman

  2. DEBII, Curtin University of Technology, Technology Park, De Laeter Way, 6102, Bentley, WA, Australia

    Tharam Dillon

  3. Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, 28660, Boadilla del Monte, Madrid, Spain

    Pilar Herrero

  4. Smeal College of Business, Pennsylvania State University, University Park, 16802, P.O. Box, PA, U.S.A.

    Akhil Kumar

  5. Institute of Databases and Information Systems, Ulm University, Germany

    Manfred Reichert

  6. City University of Hong Kong, Hong Kong

    Li Qing

  7. National University of Singapore (NUS), Singapore

    Beng-Chin Ooi

  8. Dipartemento Tecnologie dell’Informazione, Universitá degli Studi di Milano, Via Bramante 65, 26013, Crema, Italy

    Ernesto Damiani

  9. Vanderbilt University, VU, Station B #1829, 2015 Terrace Place, 37203, Nashville, TN, USA

    Douglas C. Schmidt

  10. Virginia Tech, 24060, Blacksburg, VA

    Jules White

  11. Digital Enterprise Research Institute (DERI),, National University of Ireland, IDA Business Park, Lower Dangan, Galway, Ireland

    Manfred Hauswirth

  12. Kno.e.sis Center, Wright State University, Dayton,, Ohio

    Pascal Hitzler

  13. IBM India Research Lab, 4, Block C, Institutional Area, 110 070, Vasant Kunj, New Delhi, India

    Mukesh Mohania

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Fleischhacker, D., Völker, J. (2011). Inductive Learning of Disjointness Axioms. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2011. OTM 2011. Lecture Notes in Computer Science, vol 7045. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25106-1_20

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  • DOI: https://doi.org/10.1007/978-3-642-25106-1_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25105-4

  • Online ISBN: 978-3-642-25106-1

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