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Knowledge-Base Revision Using Implications as Hypotheses

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7526))

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Abstract

In semantic integration scenarios, the integration of an assertion from some sender into the knowledge base (KB) of a receiver may be hindered by inconsistencies due to ambiguous use of symbols; hence a revision of the KB is needed to preserve its consistency. This paper analyses the new family of implication based revision operators, which exploit the idea of revising hypotheses on the semantic relatedness of the receiver’s and sender’s symbols. In order to capture the specific inconsistency resolution strategy of these operators, the novel concept of uniform sets, which are based on prime implicates, is elaborated. According to two main results of this paper these operators lend themselves to practical use in systems for semantic integration: First, the operators are finitely representable. Second, the non-sceptical versions of these operators can be axiomatically characterised by postulates, which provide a full specification of the operators’ effects.

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Author information

Authors and Affiliations

  1. Institute for Software Systems (STS), Hamburg University of Technology, Hamburg, Germany

    Özgür Lütfü Özçep

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  1. Özgür Lütfü Özçep
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Editors and Affiliations

  1. Institute of Artificial Intelligence, University of Ulm, 89069, Ulm, Germany

    Birte Glimm

  2. Saarland University and German Research Center for Artificial Intelligence (DFKI), Stuhlsatzenhausweg 3, 66123, Saarbrücken, Germany

    Antonio Krüger

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Özçep, Ö.L. (2012). Knowledge-Base Revision Using Implications as Hypotheses. In: Glimm, B., Krüger, A. (eds) KI 2012: Advances in Artificial Intelligence. KI 2012. Lecture Notes in Computer Science(), vol 7526. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33347-7_19

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  • DOI: https://doi.org/10.1007/978-3-642-33347-7_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33346-0

  • Online ISBN: 978-3-642-33347-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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