关键词: 知识图谱, 问答系统, 领域知识映射, 知识质量感知

Abstract: Knowledge Graph（KG） is one of the key technologies for implementing an intelligent Question Answering system（QA）. It can reduce customer service costs and enhance their self-service capabilities. It has a lot of commercial values and research meanings. To reduce the fuzzy of Chinese language questions and the high cost of online service operation and maintenance in a KG based QA, a smart grid customer service Question Answering system（HDKG-QA） is proposed. It is based on the hybrid domain feature of KG. It first identifies the entity based on a LSTM model. Then it proposes a semantic enhancement method based on the topic comparison to accurately find the external knowledge. It uses heuristic rules to get the optimal answer. Periodically, it updates the global KG according to the result of ILP solver. HDKG-QA can achieve high entity/predication recognition accuracy. It automatically maps domain knowledge to the local KG, and updates online KG. It can achieve high quality response with low response delay. The system is verified by the actual Q&A dataset in the State Grid Chongqing Electric Power Corporation, the experimental results show that the accuracy of the QA is improved by 17%. Through introducing LSTM and semantic enhancement methods, the heuristic rules and the sorting strategy can increase the accuracy by 8%. The Q&A response latency can be reduced by 9% with the same accuracy by introducing the ILP solver.

Key words: knowledge graph, question answering system, mapping model of domain features, knowledge quality awareness