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Abstract:
Methods for information extraction (IE) and knowledge base (KB) construction
have been intensively studied. However, a largely under-explored case is
tapping into highly dynamic sources like news streams and social media, where
new entities are continuously emerging. In this paper, we present a method for
discovering and semantically typing newly emerging out-of-
KB entities, thus improving the freshness and recall of ontology-based IE and
improving the precision and semantic rigor of open IE. Our method is based on a
probabilistic model that feeds weights into integer linear programs that
leverage type signatures of relational phrases and type correlation or
disjointness constraints. Our experimental evaluation, based on crowdsourced
user studies, show our method performing significantly better than prior work.
