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    An approach to probabilistic data integration for the semantic web

    Calì, Andrea and Lukasiewicz, T. (2008) An approach to probabilistic data integration for the semantic web. In: da Costa, P.C.G. and d'Amato, C. and Fanizzi, N. and Laskey, K.B. and Laskey, K.J. and Lukasiewicz, T. and Nickles, M. and Pool, M. (eds.) Uncertainty Reasoning for the Semantic Web I. Berlin, Germany: Springer, pp. 52-65. ISBN 9783540897644.

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    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.


    Item Type: Book Section
    Additional Information: ISWC International Workshops, URSW 2005-2007, Revised Selected and Invited Papers
    Keyword(s) / Subject(s): Probabilistic data integration, Semantic Web, probabilistic description logic programs, description logics, normal programs, answer set semantics, well-founded semantics, probabilistic uncertainty
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 14 May 2013 10:28
    Last Modified: 09 Aug 2023 12:33


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