Fast discovery of association rules
Advances in knowledge discovery and data mining
Belief revision and information fusion on optimum entropy: Research Articles
International Journal of Intelligent Systems - Uncertain Reasoning (Part 2)
Measuring inconsistency in probabilistic knowledge bases
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Inconsistency measures for probabilistic logics
Artificial Intelligence
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The focus of this paper is on the practical aspects of efficiently resolving inconsistencies when merging probabilistic rule sets. We consider the problem of prioritized merging, when one core knowledge base is to be used without modifications and to be extended by information from other sources. This problem is addressed by our flexible system Heurekathat aims at restoring consistency by finding those parts of the additional rule bases which are compatible with the core base and are considered most valuable by the user. We give an overview on the methodological framework of the system and describe some details of its main techniques. In particular, Heurekaoffers a convenient interface to inductive probabilistic reasoning on maximum entropy. An example from the domain of auditing illustrates the problem and the practical applicability of our framework.