Towards Large-scale Inconsistency Measurement
This work addresses the challenge of inconsistency measurement for large-scale knowledge bases, which is incremental as it builds on existing measures with streaming adaptations.
The paper tackles the problem of measuring inconsistency in large knowledge bases by developing a novel inconsistency measure suitable for streaming data and stream-based approximations for existing measures. The empirical analysis shows that approximations of the new measure and the contension inconsistency measure enable feasible large-scale inconsistency measurement in terms of runtime, accuracy, and scalability.
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.