Towards Large-Scale Inconsistency Measurement

• Published in: KI 2014: Advances in Artificial Intelligence pp. 195 - 206
• Main Author: Thimm, Matthias
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2014
• Series: Lecture Notes in Computer Science
• ISBN: 3319112058; 9783319112053
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-11206-0_19

We investigate the problem of inconsistency measurement on large knowledge bases by considering stream-based inconsistency measurement, i.,e., we investigate inconsistency measures that cannot consider a knowledge base as a whole but process it within a stream. For that, we present, first, a novel inconsistency measure that is apt to be applied to the streaming case and, second, stream-based approximations for the new and some existing inconsistency measures. We conduct an extensive empirical analysis on the behavior of these inconsistency measures on large knowledge bases, in terms of runtime, accuracy, and scalability. We conclude that for two of these measures, the approximation of the new inconsistency measure and an approximation of the contension inconsistency measure, large-scale inconsistency measurement is feasible.