面向知识图谱问答的查询图选择模型研究

Method of Query Graph Selection for Knowledge Base Question Answering

在基于查询图的知识图谱问答系统(KBQA)中,查询图选择旨在实现问句和候选查询图的语义匹配,从中选出最优查询图来生成答案.受限于问句(序列结构)和候选查询图(图结构)表示形式上的不统一,两者在进行语义匹配时经常存在编码结构复杂以及匹配效果差的问题.为了解决上述问题,提出一种基于序列匹配的查询图选择方法.具体地,首先将图结构的查询图转换为序列形式,使得语义匹配从图结构与序列结构的相似计算变成两个序列结构之间的相似计算,提高了匹配效果.在此基础上,通过考虑候选查询图集合的全局信息,提出一种新的查询图排序模型.与已往方法相比,所提方法一方面有效地对问句和查询图之间交互信息进行建模,另一方面引入候选查询图集合的全局信息,提升了查询图选择的性能.实验结果表明,所提方法在WebQuestions和ComplexQuestions两个常用KBQA数据集上的F1值分别达到了55.3和44.4.

Query graph selection realizes the semantic matching between question and candidate query graphs for knowledge base question answering(KBQA)based on query graph.And it selects the optimal query graph to generate answers.Due to the inconsistency in the form of question(sequence structure)and candidate query graph(graph structure),the matching often suffers from complex encoding structure and poor matching performance.To tackle the above problems,we propose a query graph selection method based on sequence matching.Specifically,we linearize the query graph into a sequence and translate the matching between the question and the query graph into the matching between two sequences.Besides,we propose a new query graph ranking model by considering the global information of the candidate query graphs.Compared with previous methods,the proposed method not only effectively models the interactive information between question and query graph,but also introduces the global information of candidate query graphs to improve the performance of query graph selection.The experimental results show that the F1 of this system on WebQuestions and ComplexQuestions are 55.3 and 44.4 respectively.