Artificial Intelligence
Two Information Measures for Inconsistent Sets
Journal of Logic, Language and Information
Measuring inconsistency in knowledge via quasi-classical models
Eighteenth national conference on Artificial intelligence
Formal methods for the validation of automotive product configuration data
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
How to act on inconsistent news: ignore, resolve, or reject
Data & Knowledge Engineering
Measuring inconsistency in knowledgebases
Journal of Intelligent Information Systems
Analysing inconsistent first-order knowledgebases
Artificial Intelligence
Measuring Inconsistency for Description Logics Based on Paraconsistent Semantics
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Completing description logic knowledge bases using formal concept analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Quantifying information and contradiction in propositional logic through test actions
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Measuring Inconsistency in DL-Lite Ontologies
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
An Anytime Algorithm for Computing Inconsistency Measurement
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Clone: solving weighted Max-SAT in a reduced search space
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Approaches to measuring inconsistent information
Inconsistency Tolerance
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Knowledge base metrics provide a useful way to analyze and compare knowledge bases. For example, inconsistency measurements have been proposed to distinguish different inconsistent knowledge bases. Whilst inconsistency degrees have been widely developed, the incompleteness of a knowledge base is rarely studied due to the difficulty of formalizing incompleteness. For this, we propose an incompleteness degree based on multi-valued semantics and show that it satisfies some desired properties. Moreover, we develop an algorithm to compute the proposed metric by reducing the problem to an instance of partial MaxSAT problem such that we can benefit from highly optimized partial MaxSAT solvers. We finally examine the approach over a set of knowledge bases from real applications, which experimentally shows that the proposed incompleteness metric can be computed practically.