ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (3): 582-600.doi: 10.7544/issn1000-1239.2016.20148228

• 人工智能 • 上一篇    下一篇

知识图谱构建技术综述

刘峤,李杨,段宏,刘瑶,秦志光   

  1. (电子科技大学信息与软件工程学院 成都 610054) (qliu@uestc.edu.cn)
  • 出版日期: 2016-03-01
  • 基金资助: 
    国家“八六三”高技术研究发展计划基金项目(2011AA010706);国家自然科学基金项目(61133016,61272527);教育部-中国移动科研基金项目(MCM20121041)

Knowledge Graph Construction Techniques

LiuQiao,LiYang,DuanHong,LiuYao,QinZhiguang   

  1. (School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054)
  • Online: 2016-03-01

摘要: 谷歌知识图谱技术近年来引起了广泛关注,由于公开披露的技术资料较少,使人一时难以看清该技术的内涵和价值.从知识图谱的定义和技术架构出发,对构建知识图谱涉及的关键技术进行了自底向上的全面解析.1)对知识图谱的定义和内涵进行了说明,并给出了构建知识图谱的技术框架,按照输入的知识素材的抽象程度将其划分为3个层次:信息抽取层、知识融合层和知识加工层;2)分别对每个层次涉及的关键技术的研究现状进行分类说明,逐步揭示知识图谱技术的奥秘,及其与相关学科领域的关系;3)对知识图谱构建技术当前面临的重大挑战和关键问题进行了总结.

关键词: 知识图谱, 语义网, 信息检索, 语义搜索引擎, 自然语言处理

Abstract: Google’s knowledge graph technology has drawn a lot of research attentions in recent years. However, due to the limited public disclosure of technical details, people find it difficult to understand the connotation and value of this technology. In this paper, we introduce the key techniques involved in the construction of knowledge graph in a bottom-up way, starting from a clearly defined concept and a technical architecture of the knowledge graph. Firstly, we describe in detail the definition and connotation of the knowledge graph, and then we propose the technical framework for knowledge graph construction, in which the construction process is divided into three levels according to the abstract level of the input knowledge materials, including the information extraction layer, the knowledge integration layer, and the knowledge processing layer, respectively. Secondly, the research status of the key technologies for each level are surveyed comprehensively and also investigated critically for the purposes of gradually revealing the mysteries of the knowledge graph technology, the state-of-the-art progress, and its relationship with related disciplines. Finally, five major research challenges in this area are summarized, and the corresponding key research issues are highlighted.

Key words: knowledge graph, semantic Web, information retrieval, semantic search engine, natural language processing

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