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Consistency of test cases in validation of Bayesian knowledge-bases

Authors: Eugene Santos; Hang Dinh;

Consistency of test cases in validation of Bayesian knowledge-bases

Abstract

Knowledge validation is a vital phase in knowledge engineering which is ultimately aimed at correcting the knowledge-base such that when inferenced over, it satisfy all test cases specified by the expert users. Naturally, such a goal is unachievable if there is some contradiction in the given set of test cases. In this work, we analyze this property of test case sets in knowledge validation for knowledge bases that are modelled in terms of Bayesian knowledge bases (BKBs) in order to determine the necessary and sufficient condition for a test case set to be contradiction-free, i.e., "Does there exist a knowledge base satisfying all test cases in the set?" We show that the complexity of deciding if a test case set meets that condition is NP-compete. As such, we also present some special cases in which it is tractable to make this determination.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Average
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