ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2022, Vol. 59 ›› Issue (9): 1947-1965.doi: 10.7544/issn1000-1239.20210829

• 人工智能 • 上一篇    下一篇

新一代知识图谱关键技术综述

王萌1,王昊奋2,李博涵3,赵翔4,王鑫5   

  1. 1(东南大学计算机科学与工程学院 南京 211189);2(同济大学创意设计学院 上海 200092);3(南京航空航天大学计算机学院 南京 211106);4(国防科技大学信息系统工程国家重点实验室 长沙 410073);5(天津大学智能与计算学部 天津 300072) (meng.wang@seu.edu.cn)
  • 出版日期: 2022-09-01
  • 基金资助: 
    国家重点研发计划项目(2019YFE0198600);国家自然科学基金项目(61906037,62176185,62072099,61872446,61972275);中央高校基本科研业务费专项资金(22120210109)

Survey on Key Technologies of New Generation Knowledge Graph

Wang Meng1, Wang Haofen2, Li Bohan3, Zhao Xiang4, Wang Xin5   

  1. 1(School of Computer Science and Engineering, Southeast University, Nanjing 211189);2(College of Design and Innovation, Tongji University, Shanghai 200092);3(College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106);4(Science and Technology on Information Systems Laboratory (National University of Defense Technology), Changsha 410073);5(College of Intelligence and Computing, Tianjin University, Tianjin 300072)
  • Online: 2022-09-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2019YFE0198600), the National Natural Science Foundation of China (61906037, 62176185, 62072099, 61872446, 61972275), and the Fundamental Research Funds for the Central Universities (22120210109).

摘要: 近年来,国内外在新一代知识图谱的关键技术和理论方面取得了一定进展,以知识图谱为载体的典型应用也逐渐走进各个行业领域,包括智能问答、推荐系统、个人助手等.然而,在大数据环境和新基建背景下,数据对象和交互方式的日益丰富和变化,对新一代知识图谱在基础理论、体系架构、关键技术等方面提出新的需求,带来新的挑战.将综述国内外新一代知识图谱的关键技术研究发展现状,重点从非结构化多模态数据组织与理解、大规模动态图谱表示学习与预训练模型、神经符号结合的知识更新与推理3方面对国内外研究的最新进展进行归纳、比较和分析.最后,就未来的技术挑战和研究方向进行展望.

关键词: 知识图谱, 多模态数据, 表示学习, 预训练模型, 认知智能, 神经符号系统

Abstract: With the wave of the past decade, the development of artificial intelligence is in the critical period from perceptual intelligence to cognitive intelligence. Knowledge graph, as the core technique of knowledge engineering in the era of big data, is the combination of symbolism and connectionism, and is the cornerstone of realizing cognitive intelligence. It provides an effective solution for the knowledge organization and intelligent application in the Internet era. In recent years, some progress has been made in the key technologies and theories of knowledge graph, and typical applications of knowledge graph based on information system have gradually entered various industries, including intelligent question answering, recommendation system, personal assistant, etc. However, in the context of big data environment and new infrastructure of China, the increasing multi-modal data and new interaction ways have raised new demands and brought new challenges to the new generation of knowledge graph in terms of basic theory, architecture, and key technologies. We summarize the research and development status of key technologies of the new generation knowledge graph at home and abroad, including unstructured multi-modal data organization and understanding, large-scale dynamic knowledge graph representation learning and pre-training models, and neural-symbolic knowledge inference. We summarize, compare and analyze the latest research progress. Finally, the future technical challenges and research directions are prospected.

Key words: knowledge graph, multi-modal data, representation learning, pre-training models, cognitive intelligence, neural-symbolic systems

中图分类号: