Measuring and repairing inconsistency in probabilistic knowledge bases

By David Picado-Muiño. 2011

57 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Probabilistic & Bayesian Methods, Knowledge Graphs & Representation

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Citations in the corpus

Enriching a Model's Notion of Belief using a Persistent Memory (2021, methodology): “In the area of formal knowledge-bases (KBs), efficient algorithms have been developed for detecting, measuring, and resolving inconsistency (Hansen and Jaumard, 2000; Andersen and Pretolani, 2001; Thimm, 2009; Muiño, 2011; Thimm, 2013).”
A Review on Language Models as Knowledge Bases (2022, background): “detect inconsistencies in KBs (Andersen and Pretolani, 2001) so such conflicts can be easily resolved, while other work aimed at quantifying the degree of which inconsistency arises in KBs (Picado-Muiño, 2011).”