面向图神经网络的知识图谱嵌入研究进展  

Advances in Knowledge Graph Embedding Based on Graph Neural Networks

作　　者：延照耀 丁苍峰 马乐荣[1] 曹璐 游浩 YAN Zhaoyao;DING Cangfeng;MA Lerong;CAO Lu;YOU Hao(College of Mathematics and Computer Science,Yan'an University,Yan'an,Shaanxi 716000,China)

机构地区：[1]延安大学数学与计算机科学学院,陕西延安716000

出　　处：《计算机科学与探索》2023年第8期1793-1813,共21页Journal of Frontiers of Computer Science and Technology

基　　金：国家自然科学基金(62262067,61866038);陕西省级人才项目(YAU202213065,CXY202107);陕西省教育厅自然科学专项(22JK0622);延安大学十四五重大科研项目(2021ZCQ012)。

摘　　要：随着图神经网络的发展,基于图神经网络的知识图谱嵌入方法日益受到研究人员的关注。相比传统的方法,它可以更好地处理实体的多样性和复杂性,并捕捉实体的多重特征和复杂关系,从而提高知识图谱的表示能力和应用价值。首先概述知识图谱的发展历程,梳理知识图谱和图神经网络的基本概念;其次着重讨论基于图卷积、图神经、图注意力以及图自编码器的知识图谱嵌入的设计思路和算法框架;然后描述图神经网络的知识图谱嵌入在链接预测、实体对齐、知识推理以及知识图谱补全等任务中的性能,同时补充图神经网络在常识性知识图谱中的一些研究;最后进行全面性的总结,并针对知识图谱嵌入存在的一些问题和挑战,勾画未来研究方向。As graph neural networks continue to develop,knowledge graph embedding methods based on graph neural networks are receiving increasing attention from researchers.Compared with traditional methods,they can better handle the diversity and complexity of entities,and capture the multiple features and complex relationships of entities,thereby improving the representation ability and application value of knowledge graphs.This paper firstly outlines the development history of knowledge graphs and the basic concepts of knowledge graphs and graph neural networks.Secondly,it focuses on discussing the design ideas and algorithm frameworks of knowledge graph embedding based on graph convolution,graph neural networks,graph attention,and graph autoencoders.Then,it describes the performance of graph neural network knowledge graph embedding in tasks such as link prediction,entity alignment,knowledge graph reasoning,and knowledge graph completion,while supplementing some research on commonsense knowledge graphs with graph neural networks.Finally,this paper makes a comprehensive summary,and future research directions are outlined with respect to some challenges and issues in knowledge graph embedding.

关 键 词：知识图谱 知识图谱嵌入 图神经网络 表示学习 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]