Fuzzy set theory in medical diagnosis
IEEE Transactions on Systems, Man and Cybernetics
A theory of diagnosis from first principles
Artificial Intelligence
Convex Optimization
A fine-grained approach to resolving unsatisfiable ontologies
Journal on data semantics X
Measuring inconsistency in probabilistic knowledge bases
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
The consistency of the CADIAG-2 knowledge base: a probabilistic approach
LPAR'10 Proceedings of the 17th international conference on Logic for programming, artificial intelligence, and reasoning
Repairing unsatisfiable concepts in OWL ontologies
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Approaches to measuring inconsistent information
Inconsistency Tolerance
Artificial Intelligence in Medicine
Measuring and repairing inconsistency in knowledge bases with graded truth
Fuzzy Sets and Systems
Formal approaches to rule-based systems in medicine: The case of CADIAG-2
International Journal of Approximate Reasoning
Inconsistency measures for probabilistic logics
Artificial Intelligence
Policy-based inconsistency management in relational databases
International Journal of Approximate Reasoning
Approaches to measuring inconsistency for stratified knowledge bases
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
In this paper we present a family of measures aimed at determining the amount of inconsistency in probabilistic knowledge bases. Our approach to measuring inconsistency is graded in the sense that we consider minimal adjustments in the degrees of certainty (i.e., probabilities in this paper) of the statements necessary to make the knowledge base consistent. The computation of the family of measures we present here, in as much as it yields an adjustment in the probability of each statement that restores consistency, provides the modeler with possible repairs of the knowledge base. The case example that motivates our work and on which we test our approach is the knowledge base of CADIAG-2, a well-known medical expert system.