知识库问答系统研究进展

Research Progress of Knowledge Base Question Answering

近年来,涌现了很多高质量大规模的知识库,基于知识库的问答系统(Knowledge Base Question Answering,KBQA)随着知识库的发展而得到了快速发展.知识库问答系统通过对自然语言问句进行理解和解析,进而利用知识库中的事实来回答自然语言问题,使用户在不了解知识库数据结构的情况下快速、精准的得到有价值的知识或答案.本文对知识库问答系统的研究方法进行了详细介绍并对目前的研究进展进行了总结,包括基于模板的方法、基于语义解析的方法和基于深度学习的方法.通过对这些研究方法进行对比,指出了各方法中存在的问题和不足,进而对知识库问答系统所面临的问题和挑战进行了总结.

In recent years,with the rapid development of knowledge base,many high-quality,large-scale knowledge bases have emerged,and the Knowledge Base Question Answering(KBQA)system has developed rapidly with the development of the knowledge base.The KBQA system understands and parses natural language questions,and then uses facts in the knowledge base to automatically answer natural language questions.Users can quickly and accurately obtain valuable knowledge or information without understanding the data structure of the knowledge base.This article introduces the research methods of the KBQA system in detail and summarizes the current research progress including query template-based methods semantic paising-based methods,and deep learning-based methods.By comparing these research methods,we point out the shortcomings and problems in each technique,and then summarize the issues and challenges faced by the KBQA system.