计算机科学 ›› 2023, Vol. 50 ›› Issue (3): 94-113.doi: 10.11896/jsjkx.220900136

• 知识图谱赋能的知识工程:理论、技术与系统专题 • 上一篇    下一篇

基于表示学习的知识图谱推理研究综述

李志飞, 赵月, 张龑   

  1. 湖北大学计算机与信息工程学院 武汉 430062
  • 收稿日期:2022-09-15 修回日期:2022-12-01 出版日期:2023-03-15 发布日期:2023-03-15
  • 通讯作者: 李志飞(zhifei1993@hubu.edu.cn)
  • 基金资助:
    国家自然科学基金(62207011,61977021,62101179);湖北省技术创新专项重大项目(2019ACA144);湖北大学校级项目(202111903000001,202011903000002)

Survey of Knowledge Graph Reasoning Based on Representation Learning

LI Zhifei, ZHAO Yue, ZHANG Yan   

  1. School of Computer Science and Information Engineering,Hubei University,Wuhan 430062,China
  • Received:2022-09-15 Revised:2022-12-01 Online:2023-03-15 Published:2023-03-15
  • About author:LI Zhifei,born in 1993,Ph.D,lecture,is a member of China Computer Federation.His main research interests include knowledge graphs and graph neural networks.
  • Supported by:
    National Natural Science Foundation of China(62207011,61977021,62101179),Key Project of Technology Innovation in Hubei Province(2019ACA144) and School Project of Hubei University(202111903000001,202011903000002).

摘要: 知识图谱以结构化形式描述了现实世界中的客观知识,但面临着构建不完整或者无法处理新增知识等挑战。知识图谱推理方法成为了知识图谱补全和更新的重要手段,该方法旨在基于图谱中已有的事实推断出未知的事实。近年来,基于表示学习的知识图谱推理研究受到了广泛关注,其主要研究思路是将实体和关系嵌入到低维连续向量空间从而进行推理,具有计算效率快、推理性能高等优势。文中以基于表示学习的知识图谱推理方法为研究对象,首先对相关的符号表示、数据集、评价指标、训练方法以及评测任务进行了简要概述;其次介绍了基于平移距离和语义匹配的两种典型知识图谱推理方法;然后对融合多源信息的推理方法进行了分类和梳理,以及详细分析了近期流行的基于神经网络的推理研究进展;最后总结全文,同时对知识图谱推理的未来研究方向进行展望。

关键词: 表示学习, 知识图谱推理, 平移距离, 语义匹配, 多源信息, 神经网络

Abstract: Knowledge graphs describe objective knowledge in the real world in a structured form,and are confronted with issues of completeness and newly-added knowledge.As an important means of complementing and updating knowledge graphs,know-ledge graph reasoning aims to infer new knowledge based on existing knowledge.In recent years,the research on knowledge graph reasoning based on repre-sentation learning has received extensive attention.The main idea is to convert the traditional reasoning process into semantic vector calculation based on the distributed representation of entities and relations.It has the advantages of fast calculation efficiency and high reasoning performance.In this paper,we review the knowledge graph reasoning based on repre sentation learning.Firstly,this paper summarizes the symbolic representation,data set,evaluation metric,training method,and evaluation task of knowledge graph reasoning.Secondly,it introduces the typical methods of knowledge graph reasoning,including translational distance and semantic matching methods.Thirdly,multi-source information fusion-based knowledge graph reasoning methods are classified.Then,neural network-based reasoning methods are introduced including convolutional neural network,graph neural network,recurrent neural network,and capsule network.Finally,this paper summarizes and forecasts the future research direction of knowledge graph reasoning.

Key words: Representation learning, Knowledge graph reasoning, Translational distance, Semantic matching, Multi-source information, Neural network

中图分类号: 

  • TP391
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