  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.
