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Dealing with Contradictory Evidence Using Fuzzy Trust in Semantic Web Data

  • Conference paper
Uncertainty Reasoning for the Semantic Web II (URSW 2010, URSW 2009, URSW 2008, UniDL 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7123))

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Abstract

Term similarity assessment usually leads to situations where contradictory evidence support has different views concerning the meaning of a concept and how similar it is to other concepts. Human experts can resolve their differences through discussion, whereas ontology mapping systems need to be able to eliminate contradictions before similarity combination can achieve high quality results. In these situations, different similarities represent conflicting ideas about the interpreted meaning of the concepts. Such contradictions can contribute to unreliable mappings, which in turn worsen both the mapping precision and recall. In order to avoid including contradictory beliefs in similarities during the combination process, trust in the beliefs needs to be established and untrusted beliefs should be excluded from the combination. In this chapter, we propose a solution for establishing fuzzy trust to manage belief conflicts using a fuzzy voting model.

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Author information

Authors and Affiliations

  1. Knowledge Media Institute (KMi), The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom

    Miklos Nagy

  2. Facultad de Ingenieria y Ciencias Centro de Investigaciones en Informatica y Telecomunicaciones, Universidad Adolfo Ibanez, Campus Vinia del Mar, Chile

    Maria Vargas-Vera

Authors
  1. Miklos Nagy
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  2. Maria Vargas-Vera
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, University of Zaragoza, Ada Byron Building, Maria de Luna 1, 50018, Zaragoza, Spain

    Fernando Bobillo

  2. Department of Systems Engineering and Operations Research, George Mason University, Engineering Building, 4400 University Drive, 22030-4444, Fairfax, VA, USA

    Paulo C. G. Costa

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

    Claudia d’Amato

  4. Dipatimento di Informatica and CILA, Università degli Studi di Bari, Via Orabona 4, 70125, Bari, Italy

    Nicola Fanizzi

  5. Department of Systems Engineering and Operations Research, George Mason University, Engineering Building, 4400 University Drive, 22030-4444, Fairfax, VA, USA

    Kathryn B. Laskey

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

    Kenneth J. Laskey

  7. Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, OX1 3QD, Oxford, UK

    Thomas Lukasiewicz

  8. Technische Universität München, Abteilung für Informatk, Boltzmannstraße 3, 85748, Garching, Germany

    Matthias Nickles

  9. Goldman Sachs, 30 Hudson Street, 07302-4699, Jersey City, NJ, USA

    Michael Pool

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Nagy, M., Vargas-Vera, M. (2013). Dealing with Contradictory Evidence Using Fuzzy Trust in Semantic Web Data. In: Bobillo, F., et al. Uncertainty Reasoning for the Semantic Web II. URSW URSW URSW UniDL 2010 2009 2008 2010. Lecture Notes in Computer Science(), vol 7123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35975-0_8

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  • DOI: https://doi.org/10.1007/978-3-642-35975-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35974-3

  • Online ISBN: 978-3-642-35975-0

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