Quantifying knowledge base inconsistency via fixpoint semantics

  • Authors:
  • Du Zhang

  • Affiliations:
  • Department of Computer Science, California State University, Sacramento, CA

  • Venue:
  • Transactions on computational science II
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

Inconsistency and its handling are very important in the real worldand in the fields of computer science and artificial intelligence. When dealingwith inconsistency in a knowledge base (KB), there is a whole host of deeperissues we need to contend with in order to develop rational and robust intelligentsystems. In this paper, we focus our attention on one of the issues in copingwith KB inconsistency: how to measure the information content and thesignificance of inconsistency in a KB. Our approach is based on a fixpoint semanticsfor KB. The approach reflects each inconsistent set of rules in the leastfixpoint of a KB and then measures the inconsistency in the context of the leastfixpoint for the KB. Compared with the existing results, our approach has someunique benefits.