Approaches to measuring inconsistency for stratified knowledge bases

  • Authors:

  • Kedian Mu;Kewen Wang;Lian Wen

  • Affiliations:

  • School of Mathematical Sciences, Peking University, Beijing 100871, PR China;School of Information and Communication Technology, Nathan Campus, Griffith University, 170 Kessels Road, Nathan, Brisbane, Queensland 4111, Australia;School of Information and Communication Technology, Nathan Campus, Griffith University, 170 Kessels Road, Nathan, Brisbane, Queensland 4111, Australia

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2014

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Abstract

A number of proposals have been proposed for measuring inconsistency for knowledge bases. However, it is rarely investigated how to incorporate preference information into inconsistency measures. This paper presents two approaches to measuring inconsistency for stratified knowledge bases. The first approach, termed the multi-section inconsistency measure (MSIM for short), provides a framework for characterizing the inconsistency at each stratum of a stratified knowledge base. Two instances of MSIM are defined: the naive MSIM and the stratum-centric MSIM. The second approach, termed the preference-based approach, aims to articulate the inconsistency in a stratified knowledge base from a global perspective. This approach allows us to define measures by taking into account the number of formulas involved in inconsistencies as well as the preference levels of these formulas. A set of desirable properties are introduced for inconsistency measures of stratified knowledge bases and studied with respect to the inconsistency measures introduced in the paper. Computational complexity results for these measures are presented. In addition, a simple but explanatory example is given to illustrate the application of the proposed approaches to requirements engineering.