Multi-space Knowledge Enhanced Question Answering over Knowledge Graph

Knowledge graph question answering (KG-QA) accesses the substantial knowledge to return a comprehensive answer in a more user-friendly solution. Recently, the embedding-based methods to KG-QA have always been hot issues. Traditional embedding-based methods can not make full use of knowledge since they incorporating the knowledge by using a single semantic translation model to embed entities and relations. Semantic translation models based on non-Euclidean spaces can capture more kinds of latent information because they can focus on the characteristics of different aspects of knowledge. In this paper, we propose the multi-space knowledge enhanced question answering model and mine the latent information of knowledge in different embedding spaces to improve the KG-QA. In addition, Transformer is used to replace the traditional Bi-LSTM to obtain the vector representation of question, and specially designed attention mechanism is used to calculate the score of candidate answers dynamically. The experiment conducted on the WebQuestions dataset shows that compared with other state-of-art QA systems, our method can effectively improve the accuracy.