On automatic knowledge validation for Bayesian knowledge bases
Data & Knowledge Engineering
Goal-driven semi-automated generation of semantic models
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
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Knowledge validation is a vital phase in knowledge engineering which is ultimately aimed at correcting the knowledge-base such that when inferenced over, it will 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.