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Ontology Learning and Reasoning — Dealing with Uncertainty and Inconsistency

  • Conference paper
Uncertainty Reasoning for the Semantic Web I (URSW 2006, URSW 2007, URSW 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5327))

Abstract

Ontology learning aims at generating domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques. It is inherent to the ontology learning process that the acquired ontologies represent uncertain and possibly contradicting knowledge. From a logical perspective, the learned ontologies are potentially inconsistent knowledge bases, that as such do not allow for meaningful reasoning. In this paper, we present an approach to generating consistent OWL ontologies from automatically generated or enriched ontology models, which takes into account the uncertainty of the acquired knowledge. We illustrate and evaluate the application of our approach with two experiments in the scenarios of consistent evolution of learned ontologies and enrichment of ontologies with disjointness axioms.

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Authors and Affiliations

  1. Institute AIFB, University of Karlsruhe, Germany

    Peter Haase & Johanna Völker

Authors
  1. Peter Haase
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  2. Johanna Völker
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Editor information

Editors and Affiliations

  1. George Mason University, 4400 University Drive, Fairfax, 22030, Virginia, USA

    Paulo Cesar G. da Costa

  2. Dipartimento di Informatica,, Università degli Studi di Bari, Via Orabona, 4, 70125, Bari, Italy

    Claudia d’Amato

  3. Dipartimento di Informatica, Università degli Studi di Bari, Via Orabona, 4, 70125, Bari, Italy

    Nicola Fanizzi

  4. George Mason University, VA 22030, Fairfax, USA

    Kathryn B. Laskey

  5. MIRTE Corporation, M/S H305, 7515 Colshire Drive, VA 22102-7508, McLean, USA

    Kenneth J. Laskey

  6. Computing Laboratory, Oxford University, Wolfson Building, Parks Road, Oxford OX1 3QD, UK

    Thomas Lukasiewicz

  7. University of Bath, BA2 7AY, Bath, UK

    Matthias Nickles

  8. Convera, Inc., 1921 Gallows Road, Suite 200, VA 22182, Vienna, USA

    Michael Pool

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Haase, P., Völker, J. (2008). Ontology Learning and Reasoning — Dealing with Uncertainty and Inconsistency. In: da Costa, P.C.G., et al. Uncertainty Reasoning for the Semantic Web I. URSW URSW URSW 2006 2007 2005. Lecture Notes in Computer Science(), vol 5327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89765-1_21

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  • DOI: https://doi.org/10.1007/978-3-540-89765-1_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89764-4

  • Online ISBN: 978-3-540-89765-1

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