Natural Language Supported Relation Matching for Question Answering with Knowledge Graphs

This work focuses on the relation matching problem in knowledge based question answering systems. Finding the right relation a natural question asks is a key step in current knowledge based question answering systems, while also being the most difficult one, because of the mismatch between natural language question and formal relation type definitions. In this paper, we present two approaches to tackle this problem. The first approach tries to directly learn the soft match between the question and the relations from the training data using neural networks. The second approach enriches the relation name with natural language support sentences generated from Wikipedia, which provide additional matches with the question. Experiments on the WebQuestions dataset demonstrate that both of our approaches improve the relation matching accuracy of a prior state-of-the-art. Our further analysis reveals the high quality of support sentences and suggests the rich potential of support sentences in question answering and semantic parsing tasks.