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Making Decisions with Knowledge Base Repairs

  • Rafael PeñalozaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Milano-BicoccaMilanItaly

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