The consistency of the CADIAG-2 knowledge base: a probabilistic approach

  • Authors:

  • Pavel Klinov;Bijan Parsia;David Picado-Muiño

  • Affiliations:

  • University of Manchester, Manchester, UK;University of Manchester, Manchester, UK;Institut für Diskrete Mathematik und Geometrie, Vienna, Austria

  • Venue:
  • LPAR'10 Proceedings of the 17th international conference on Logic for programming, artificial intelligence, and reasoning
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

The paper presents the methodology and the results of checking consistency of the knowledge base of CADIAG-2, a large-scale medical expert system. Such knowledge base consists of a large collection of rules representing knowledge about various medical entities (symptoms, signs, diseases...) and relationships between them. The major portion of the rules are uncertain, i.e., they specify to what degree a medical entity is confirmed by another medical entity or a combination of them. Given the size of the system and the uncertainty it has been challenging to validate its consistency. Recent attempts to partially formalise CADIAG-2's knowledge base into decidable Gödel logics have shown that, on that formalisation, CADIAG-2 is inconsistent. In this paper we verify this result with an alternative, more expressive formalisation of CADIAG-2 as a set of probabilistic conditional statements and apply a state-of-the-art probabilistic logic solver to determine satisfiability of the knowledge base and to extract conflicting sets of rules. As CADIAG-2 is too large to be handled out of the box we describe an approach to split the knowledge base into fragments that can be tested independently and prove that such methodology is complete (i.e., is guaranteed to find all conflicts). With this approach we are able to determine that CADIAG-2 contains numerous sets of conflicting rules and compute all of them for a slightly relaxed version of the knowledge base.