Knowledge Graph Question Answering with semantic oriented fusion model

Abstract Knowledge Graph Question Answering (KGQA) is a major branch of question answering tasks, which can answer fact questions effectively by using the reasonable characteristics of the knowledge graph. Currently, lots of related works combined with a variety of deep learning models are presented for the KGQA task. However, there are still some challenges, such as topic entity recognition under ambiguity expression, semantic level representation of natural language, efficient construction of searching space for answers, etc. In this paper, we propose a comprehensive approach for complex question answering over KG. Firstly, during the stage of topic entity recognition, a deep transition model is constructed to extract topic entities, and an efficient entity linking strategy is presented, which combines character matching and entity disambiguation model. Secondly, for candidate path ranking, a dynamic candidate path generation algorithm is proposed to efficiently create the candidate answer set. And four dedicated similarity calculation models are designed to handle the intricate condition of complex questions with long sequence and diversity expression. Moreover, a fusion policy is proposed to make decision for the final correct answer. We evaluate our approach on CKBQA, a Chinese knowledge base question answering dataset, from CCKS2019 competition. Experimental results demonstrate that the improvements in each process are effective and our approach achieves better performance than the best team in CCKS2019 competition.