  We introduce a conceptually simple and effective method to quantify the
similarity between relations in knowledge bases. Specifically, our approach is
based on the divergence between the conditional probability distributions over
entity pairs. In this paper, these distributions are parameterized by a very
simple neural network. Although computing the exact similarity is in-tractable,
we provide a sampling-based method to get a good approximation. We empirically
show the outputs of our approach significantly correlate with human judgments.
By applying our method to various tasks, we also find that (1) our approach
could effectively detect redundant relations extracted by open information
extraction (Open IE) models, that (2) even the most competitive models for
relational classification still make mistakes among very similar relations, and
that (3) our approach could be incorporated into negative sampling and softmax
classification to alleviate these mistakes. The source code and experiment
details of this paper can be obtained from
https://github.com/thunlp/relation-similarity.

Artificial Intelligence;Computation and Language;Machine Learning;Machine Learning