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计算机工程 ›› 2020, Vol. 46 ›› Issue (7): 1-13. doi: 10.19678/j.issn.1000-3428.0057869

• 热点与综述 • 上一篇    下一篇

大规模企业级知识图谱实践综述

王昊奋1, 丁军2, 胡芳槐2, 王鑫3   

  1. 1. 同济大学 设计创意学院, 上海 200092;
    2. 海乂知信息科技(南京)有限公司, 南京 210008;
    3. 天津大学 智能与计算学部, 天津 300354
  • 收稿日期:2020-03-27 修回日期:2020-05-13 发布日期:2020-04-15
  • 作者简介:王昊奋(1982-),男,特聘研究员、博士,主研方向为知识图谱、对话问答;丁军、胡芳槐,博士;王鑫,教授、博士。
  • 基金资助:
    国家自然科学基金(61972275)。

Survey on Large Scale Enterprise-level Knowledge Graph Practices

WANG Haofen1, DING Jun2, HU Fanghuai2, WANG Xin3   

  1. 1. College of Design and Innovation, Tongji University, Shanghai 200092, China;
    2. Haiyizhi Info Technology(Nanjing) Co., Ltd., Nanjing 210008, China;
    3. College of Intelligence and Computing, Tianjin University, Tianjin 300354, China
  • Received:2020-03-27 Revised:2020-05-13 Published:2020-04-15

摘要: 近年来,知识图谱及其相关技术得到快速发展,并被广泛应用于工业界各种认知智能场景中。在简述知识图谱相关研究的基础上,介绍知识图谱在工程应用中的关键技术,研究工业级知识图谱的典型应用场景与案例、具有代表性的工业级知识图谱平台以及知识图谱生命周期过程中的相关可用工具,分析企业级知识图谱平台的构建需求和面临的问题,阐述企业级知识图谱平台的构建方法及过程。针对平台化建设中遇到的问题给出相应的知识图谱中台解决方案,并对知识图谱未来的发展与挑战进行展望。

关键词: 知识图谱, 表示学习, 知识抽取, 知识存储, 知识推理, 企业级知识图谱平台, 知识图谱中台

Abstract: In recent years,knowledge graph and its related technologies have developed rapidly and have been widely used in various cognitive intelligence scenarios in industry.This paper gives a brief description of researches in knowledge graph,and on this basis introduces key technologies of knowledge graph in engineering applications.Next,the paper studies the typical application scenarios of industry knowledge graphs,the corresponding case studies supported by well-known industry knowledge graph platforms and relevant available tools in each phase of the life cycle of knowledge graphs.Then the paper analyzes requirements of constructing enterprise-level knowledge graph platforms and key problems in this process,describes construction method and process of construction.In view of the problems encountered in platform construction,this paper gives corresponding solutions of knowledge graph middle platform construction,and prospects the future development and challenges of knowledge graph.

Key words: knowledge graph, representation learning, knowledge extraction, knowledge storage, knowledge reasoning, enterprise-level knowledge graph platform, knowledge graph middle platform

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