  Knowledge graphs are widely used as a typical resource to provide answers to
factoid questions. In simple question answering over knowledge graphs, relation
extraction aims to predict the relation of a factoid question from a set of
predefined relation types. Most recent methods take advantage of neural
networks to match a question with all predefined relations. In this paper, we
propose an instance-based method to capture the underlying relation of question
and to this aim, we detect matching paraphrases of a new question which share
the same relation, and their corresponding relation is selected as our
prediction. The idea of our model roots in the fact that a relation can be
expressed with various forms of questions while these forms share lexically or
semantically similar terms and concepts. Our experiments on the SimpleQuestions
dataset show that the proposed model achieves better accuracy compared to the
state-of-the-art relation extraction models.
