计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 74-85.doi: 10.11896/jsjkx.210100122

• 智能计算 • 上一篇    下一篇


马瑞新, 李泽阳, 陈志奎, 赵亮   

  1. 大连理工大学软件学院 辽宁 大连 116620
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 赵亮(liangzhao@dlut.edu.cn)
  • 作者简介:(maruixin@dlut.edu.cn)
  • 基金资助:

Review of Reasoning on Knowledge Graph

MA Rui-xin, LI Ze-yang, CHEN Zhi-kui, ZHAO Liang   

  1. School of Software Technology,Dalian University of Technology,Dalian,Liaoning 116620,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:MA Rui-xin,born in 1975,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include big data and cloud computing.
    ZHAO Liang,born in 1988,Ph.D,associate professor.His main research inte-rests include big data and artificial intelligence.
  • Supported by:
    National Key Research and Development Program of China(2018YFC0830203),National Natural Science Foundation of China (61906030),Natural Science Foundation of Liaoning Province(2020-BS-063),Equipment Advance Research Fund(80904010301) and Fundamental Research Funds for the Central Universities(DUT20RC(4)009).

摘要: 近年来,随着互联网技术以及引用模式的快速发展,计算机世界的数据规模呈指数型增长,这些数据中蕴含着大量有价值的信息,如何从中筛选出知识并将这些知识进行有效组织和表达引起了广泛关注。知识图谱由此而生,面向知识图谱的知识推理就是知识图谱研究的热点之一,已经在语义搜索、智能问答等领域取得了重大成就。然而,由于样本数据存在各种缺陷,例如样本数据缺少头尾实体、查询路径过长、样本数据错误等,因此面对上述特点的零样本、单样本、少样本和多样本知识图谱推理更受瞩目。文中将从知识图谱的基本概念和基础知识出发,介绍近年来知识图谱推理方法的最新研究进展。具体而言,根据样本数据量大小的不同,将知识图谱推理方法分为多样本推理、少样本推理和零与单样本推理。模型使用超过5个实例数进行推理的为多样本推理,模型使用2~5实例数进行推理的为少样本推理,模型使用零个或者一个实例数进行推理的为零与单样本推理。根据方法的不同,将多样本知识图谱推理细分为基于规则的推理、基于分布式的推理、基于神经网络的推理以及基于其他的推理,将少样本知识图谱推理细分为基于元学习的推理与基于相邻实体信息的推理,具体分析总结这些方法。此外,进一步讲述了知识图谱推理的典型应用,并探讨了知识图谱推理现存的问题、未来的研究方向和前景。

关键词: 单样本推理, 多样本推理, 零样本推理, 少样本推理, 知识图谱, 知识推理

Abstract: In recent years,the rapid development of Internet technology and reference models has led to an exponential growth in the scale of computer world data,which contains a lot of valuable information.How to select knowledge from it,and organize and express this knowledge effectively attract wide attention.Knowledge graphs are also born from this.Knowledge reasoning for knowledge graphs is one of the hotspots of knowledge graph research,and important achievements are obtained in the fields of semantic search and intelligent question answering.However,due to various defects in the sample data,such as the lack of head and tail entities in sample data,the long query path,as well as the wrong sample data.In the face of the above characteristics,the knowledge graph reasoning of zero-shot,one-shot,few-shot and multi-shot get more attention.Based on the basic concepts and basic knowledge of knowledge graph,this paper introduces the latest research progress of knowledge graph reasoning methods in recent years.Specifically,according to the size of sample data,the knowledge graph reasoning method is divided into multi-shot reasoning,few-shot reasoning,zero-shot and single-shot reasoning.Models that use more than five instances for reasoning are multi-sample reasoning,models that use two to five instances for reasoning are few-shot reasoning,and those use zero or one instance number for reasoning are zero-shpt and one-shot reasoning.The multi-shot knowledge graph reasoning is subdivided into rule-based reasoning,distributed-based reasoning,neural network-based reasoning,and other reasoning.The few-shot knowledge graph reasoning is subdivided into meta-learning-based reasoning and neighboring entity information-based reasoning.And these methods are analyzed and summarized.In addition,this paper further describes the typical application of knowledge graph reaso-ning,and discusses the existing problems,future research directions and prospects of knowledge graph reasoning.

Key words: Few-shot reasoning, Knowledge graph, Knowledge reasoning, Multi-shot reasoning, One-shot reasoning, Zero-shot reasoning


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