  Open information extraction (OIE) methods extract plenty of OIE triples <noun
phrase, relation phrase, noun phrase> from unstructured text, which compose
large open knowledge bases (OKBs). Noun phrases and relation phrases in such
OKBs are not canonicalized, which leads to scattered and redundant facts. It is
found that two views of knowledge (i.e., a fact view based on the fact triple
and a context view based on the fact triple's source context) provide
complementary information that is vital to the task of OKB canonicalization,
which clusters synonymous noun phrases and relation phrases into the same group
and assigns them unique identifiers. However, these two views of knowledge have
so far been leveraged in isolation by existing works. In this paper, we propose
CMVC, a novel unsupervised framework that leverages these two views of
knowledge jointly for canonicalizing OKBs without the need of manually
annotated labels. To achieve this goal, we propose a multi-view CH K-Means
clustering algorithm to mutually reinforce the clustering of view-specific
embeddings learned from each view by considering their different clustering
qualities. In order to further enhance the canonicalization performance, we
propose a training data optimization strategy in terms of data quantity and
data quality respectively in each particular view to refine the learned
view-specific embeddings in an iterative manner. Additionally, we propose a
Log-Jump algorithm to predict the optimal number of clusters in a data-driven
way without requiring any labels. We demonstrate the superiority of our
framework through extensive experiments on multiple real-world OKB data sets
against state-of-the-art methods.

Computation and Language;Artificial Intelligence