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Making Decisions with Knowledge Base Repairs

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Modeling Decisions for Artificial Intelligence (MDAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11676))

Abstract

Building large knowledge bases (KBs) is a fundamental task for automated reasoning and intelligent applications. Needing the interaction between domain and modeling knowledge, it is also error-prone. In fact, even well-maintained KBs are often found to lead to unwanted conclusions. We deal with two kinds of decisions associated with faulty KBs. First, which portions of the KB (and their conclusions) can still be trusted? Second, which is the correct way to repair the KB? Our solution to both problems is based on storing all the information about repairs in a compact data structure.

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Author information

Authors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Rafael Peñaloza

Authors
  1. Rafael Peñaloza
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Correspondence to Rafael Peñaloza .

Editor information

Editors and Affiliations

  1. Maynooth University, Maynooth, Ireland

    Vicenç Torra

  2. Tamagawa University, Machida, Tokyo, Japan

    Yasuo Narukawa

  3. University of Milano-Bicocca, Milan, Italy

    Gabriella Pasi

  4. University of Milano-Bicocca, Milan, Italy

    Marco Viviani

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Peñaloza, R. (2019). Making Decisions with Knowledge Base Repairs. In: Torra, V., Narukawa, Y., Pasi, G., Viviani, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2019. Lecture Notes in Computer Science(), vol 11676. Springer, Cham. https://doi.org/10.1007/978-3-030-26773-5_23

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  • DOI: https://doi.org/10.1007/978-3-030-26773-5_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26772-8

  • Online ISBN: 978-3-030-26773-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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