Knowledge Verification

The goal is to define and develop algorithms and techniques for verifying the consistency of medical knowledge formalized in a crisp or fuzzy knowledge base and, thus, for detecting structural errors and the set of rules causing them.

We are investigating the problem of the consistency of a flat or chained collection of crisp/fuzzy rules, where consistency means that there does not exist any input fact in agreement with the integrity constraints that, together with the base of rules, leads to a partially or totally inconsistent knowledge base.


  • M. Esposito, D. Maisto: Structural verification through similarity measures for fuzzy rule bases representing clinical guidelines. Journal of Intelligent and Fuzzy Systems, IOS Press, DOI 10.3233/IFS-2012-0523.
  • M. Esposito, D. Maisto, A verification method for clinical guidelines represented as fuzzy rules. In The Second International Fuzzy Systems Symposium FUZZYSS'11, November 17-18, Hacettepe University, Turkey.
  • M. Esposito, D. Maisto, A Framework for Verification of Fuzzy Rule Bases Representing Clinical Guidelines. In CISSE11: Proceedings of the International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering, December 3-12
  • E. Cesario, M. Esposito, G. De Pietro, D. Talia: A Consistency Checker for Verifying the Knowledge Encoded into Clinical DSSs. Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2012), June 20-22 2012, Rome, Italy.
  • E. Cesario, M. Esposito, G. De Pietro, D. Talia: Verification of Clinical Guidelines Encoded Into Knowledge-Based DSSs. Proceedings of the 6th IEEE International Conference on Intelligent Systems: Methodology, Models, Applications in Emerging Technologies (IS’12), September 6-8 2012, Sofia, Bulgaria.