Staged query graph generation based on answer type for question answering over knowledge base

Question answering over knowledge base (KBQA) enables users to query over the knowledge base without the need to know the details. A range of existing KBQA approaches treats the entities mentioned in the given question as the starting point to find the answers. While helpful in achieving improvements on the existing benchmarks, they have some limitations on the strategy of query graph generation, which creates too many candidate queries and makes it hard to select the best-matching one to get the answer. In this paper, we propose a staged query graph generation approach based on the answer type, which exploits the correlation between questions and answer types to reduce the size of the candidate set and further improve the performance. Besides, we construct a question/answer-type (QAT) dataset aiming to predict the answer type of a given question. Extensive experiments demonstrate our method outperforms existing methods on both simple questions and complex questions.