Towards Large-Scale Inconsistency Measurement

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 i...

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Published inKI 2014: Advances in Artificial Intelligence pp. 195 - 206
Main Author Thimm, Matthias
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2014
SeriesLecture Notes in Computer Science
Online AccessGet full text
ISBN3319112058
9783319112053
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-11206-0_19

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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.
AbstractList 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.
Author Thimm, Matthias
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Copyright Springer International Publishing Switzerland 2014
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Editor Lutz, Carsten
Thielscher, Michael
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