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An Approach to Probabilistic Data Integration for the Semantic Web

  • 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

Probabilistic description logic programs are a powerful tool for knowledge representation in the Semantic Web, which combine description logics, normal programs under the answer set or well-founded semantics, and probabilistic uncertainty. The task of data integration amounts to providing the user with access to a set of heterogeneous data sources in the same fashion as when querying a single database, that is, through a global schema, which is a common representation of all the underlying data sources. In this paper, we make use of probabilistic description logic programs to model expressive data integration systems for the Semantic Web, where constraints are expressed both over the data sources and the global schema. We describe different types of probabilistic data integration, which aim especially at applications in the Semantic Web.

This paper is a significantly extended and revised version of a position paper that appeared in: Proceedings URSW-2006. CEUR Workshop Proceedings 218, CEUR-WS.org, 2006.

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Author information

Authors and Affiliations

  1. Oxford-Man Institute of Quantitative Finance, University of Oxford, Blue Boar Court, 9 Alfred Street, Oxford, OX1 4EH, UK

    Andrea Calì

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

    Andrea Calì & Thomas Lukasiewicz

Authors
  1. Andrea Calì
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  2. Thomas Lukasiewicz
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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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Calì, A., Lukasiewicz, T. (2008). An Approach to Probabilistic Data Integration for the Semantic Web. 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_4

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  • DOI: https://doi.org/10.1007/978-3-540-89765-1_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89764-4

  • Online ISBN: 978-3-540-89765-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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