Decision: conferenceOral
Abstract: The purpose of this paper is to begin a conversation about the
importance and role of confidence estimation in knowledge bases
(KBs). KBs are never perfectly accurate, yet without confidence
reporting their users are likely to treat them as if they were,
possibly with serious real-world consequences. We define a notion
of confidence based on the probability of a KB fact being true. For
automatically constructed KBs we propose several algorithms for
estimating this confidence from pre-existing probabilistic models of
data integration and KB construction. In particular, this paper
focusses on confidence estimation in entity resolution. A goal of
our exposition here is to encourage creators and curators of KBs to
include confidence estimates for entities and relations in their
KBs.
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