Relation extraction from simple questionsaims to capture the relation of a factoid question with one underlying relation from a set of predefined ones in a knowledge base . Most recent methods take advantage of neural networks for matching a question with all relations in order to find the best relation that is expressed by that question . In this paper, we propose an instance-based method to find similar questions of a new question , in the sense of their relations, to predict its mentioned relation . The motivation roots in the fact that a relation can be expressed with different forms of question and these forms mostly share similar terms or concepts. Our experiments on the SimpleQuestions dataset show that the proposed model achieved better accuracy compared to the state-of-the-art relation extraction models.
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.
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Relation extraction from simple questionsaims to capture
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
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with one underlying relation
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ones in a knowledge base
relation types
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for matching
to match
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relations in order to find the best relation that is expressed by that question
predefined relations
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find similar questions of
capture the underlying relation of question and to this aim, we detect matching paraphrases of
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, in the sense of their relations, to predict its mentioned relation . The motivation
which share the same relation, and their corresponding relation is selected as our prediction. The idea of our model
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different forms of question and these forms mostly share similar terms or
various forms of questions while these forms share lexically or semantically similar terms and
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achieved
achieves