Abstract
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.
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Thimm, M. (2014). Towards Large-Scale Inconsistency Measurement. In: Lutz, C., Thielscher, M. (eds) KI 2014: Advances in Artificial Intelligence. KI 2014. Lecture Notes in Computer Science(), vol 8736. Springer, Cham. https://doi.org/10.1007/978-3-319-11206-0_19
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DOI: https://doi.org/10.1007/978-3-319-11206-0_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11205-3
Online ISBN: 978-3-319-11206-0
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