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Inconsistency-based Ranking of Knowledge Bases

Saïd Jabbour,Badran Raddaoui,L. Sais

Published 2015 in International Conference on Agents and Artificial Intelligence

ABSTRACT

Inconsistencies are a usually undesirable feature of many kinds of data and knowledge. Measuring inconsistency is potentially useful to determine which parts of the data or of the knowledge base are conflicting. Several measures have been proposed to quantify such inconsistencies. However, one of the main problems lies in the difficulty to compare their underlying quality. Indeed, a highly inconsistent knowledge base with respect to a given inconsistency measure can be considered less inconsistent using another one. In this paper, we propose a new framework allowing us to partition a set of knowledge bases as a sequence of subsets according to a set of inconsistency measures, where the first element of the partition corresponds to the most inconsistent one. Then we discuss how finer ranking between knowledge bases can be derived from an original combination of existing measures. Finally, we extend our framework to provide some inconsistency measures obtained by combining existing ones.

PUBLICATION RECORD

  • Publication year

    2015

  • Venue

    International Conference on Agents and Artificial Intelligence

  • Publication date

    2015-01-10

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.5220/0005210704140419
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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