Research Progress of Graph Neural Network in Knowledge Graph Construction and Application

doi:10.3778/j.issn.1673-9418.2302059

Abstract

Abstract: As an effective representation of knowledge, knowledge graph network can be used to represent rich factual information between different categories and become an effective knowledge management tool. It has achieved great results in the application and research of knowledge engineering and artificial intelligence. Know-ledge graph is usually expressed as a complex network structure. Its unstructured characteristics make the applica-tion of graph neural network to the analysis and research of knowledge graph become a research hotspot in academia. The purpose of this paper is to provide extensive research on knowledge graph construction technology based on graph neural network to solve two types of knowledge graph construction tasks, including knowledge extraction (entity, relationship and attribute extraction) and knowledge merging and processing (link prediction, entity alignment and knowledge reasoning, etc.). Through these tasks, the structure of knowledge graph can be further improved and new knowledge and reasoning relationships can be discovered. This paper also studies the advanced graph neural network method for knowledge graph related applications, such as recommendation system, question answering system and computer vision. Finally, the future research directions of knowledge graph application based on graph neural network are proposed.

Key words: knowledge graph, graph neural network, construction technology

摘要： 作为知识的一种有效的表征方式，知识图谱网络可以用于表示不同类别之间丰富的事实信息，成为有效的知识管理工具，并在知识工程和人工智能领域的应用和研究取得了较大的成果。知识图谱通常表现为一种复杂的网络结构，其非结构化特点使得将图神经网络应用于知识图谱的分析和研究成为学术界的研究热点。旨在对基于图神经网络的知识图谱构建技术提供广泛、全面的研究，以解决两类知识图谱构建的任务，包括知识抽取（实体、关系和属性抽取）和知识合并与加工（链接预测、实体对齐和知识推理等），通过这些任务，可以进一步完善知识图谱的结构，并能够发现新的知识和推理关系。还研究了基于高级的图神经网络方法用于知识图谱相关的应用，如推荐系统、问答系统和计算机视觉等。最后提出了基于图神经网络的知识图谱应用的未来研究方向。

关键词: 知识图谱, 图神经网络, 构建技术

XU Xinran, WANG Tengyu, LU Cai. Research Progress of Graph Neural Network in Knowledge Graph Construction and Application[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(10): 2278-2299.

许鑫冉, 王腾宇, 鲁才. 图神经网络在知识图谱构建与应用中的研究进展[J]. 计算机科学与探索, 2023, 17(10): 2278-2299.

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