Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (10): 1843-1869.DOI: 10.3778/j.issn.1673-9418.2106095

• Knowledge-Based Question Answering Systems • Previous Articles     Next Articles

Survey of Open-Domain Knowledge Graph Question Answering

CHEN Zirui, WANG Xin, WANG Lin, XU Dawei, JIA Yongzhe   

  1. 1. College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
    2. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin 300350, China
    3. Tianjin TechFantasy Co., Ltd., Tianjin 300457, China
  • Online:2021-10-01 Published:2021-09-30



  1. 1. 天津大学 智能与计算学部,天津 300350
    2. 天津市认知计算与应用重点实验室,天津 300350
    3. 天津泰凡科技有限公司,天津 300457


Knowledge graph question answering (KGQA) is the procedure of processing natural language questions posed by users to obtain relevant answers from knowledge graph (KG) based on some form of KG. Due to the limitation of knowledge scale, computing power and natural language processing capability, the early knowledge base question answering systems were limited to closed-domain questions. In recent years, with the development of KG and the construction of open-domain question answering (QA) datasets, KG has been used for open-domain QA research and practice. In this paper, in accordance with the development of technology, the open-domain KGQA is summarized. Firstly, five rule and template based KGQA methods are reviewed, including traditional semantic parsing, traditional information retrieval, triplet matching, utterance template, and query template. This type of methods mainly relies on manually defined rules and templates to complete QA task. Secondly, five deep learning based KGQA methods are introduced, which use neural network models to complete the subtasks of QA process, including knowledge graph embedding, memory network, neural network-based semantic parsing, neural network-based query graph, and neural network-based information retrieval method. Thirdly, four general domain KG and eleven open-domain QA datasets, which KGQA commonly used are described. Fourthly, three classic KGQA datasets are selected according to the difficulty of questions to compare and analyze the performance metric of each KGQA system, and the effect between above methods. Finally, this paper looks forward to the future research directions on this topic.

Key words: knowledge graph question answering (KGQA), open-domain, deep learning, semantic parsing, information retrieval



关键词: 知识图谱问答(KGQA), 开放领域, 深度学习, 语义解析, 信息检索