Computational approaches to finding and measuring inconsistency in arbitrary knowledge bases

Kevin McAreavey, Weiru Liu, Paul Miller

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)
167 Downloads (Pure)

Abstract

Originality: In this paper, we develop one of the first computationally tractable approaches to finding minimal inconsistent subsets in knowledge bases.
Significance: Our approach has enabled us to develop a tool for inconsistency handling that can formally verify security rule-sets used in applications such as network intrusion detection and access control.
Rigour: The proposed approach is based on a rigorous formal mathematical treatment using propositional logic. Furthermore, the first exhaustive experimental evaluation of an inconsistency tool using a large dataset of randomly generated knowledge bases is performed.
Original languageEnglish
Pages (from-to)1659-1693
Number of pages35
JournalInternational Journal of Approximate Reasoning
Volume55
Issue number8
DOIs
Publication statusPublished - Nov 2014

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