Applications of Deep Learning in Knowledge Graph Construction and Reasoning

doi:10.3778/j.issn.1002-8331.2408-0280

Abstract

Abstract: Knowledge graphs, as a structured form of knowledge representation in the field of natural language processing, can describe concepts and their relationships in the real world, and is often used in information retrieval, data management, and other fields. Deep learning has gradually become an emerging research hotspot due to its ability to automatically learn the underlying patterns and hierarchical representations from diverse data, which can be used for precise construction and effective reasoning of large-scale, high-quality knowledge graphs. To further promote the technological integration of deep learning and knowledge graphs, this paper focuses on the construction and reasoning processes of knowledge graphs, providing a comprehensive introduction to the relevant theories and latest research achievements in the fields of knowledge representation, knowledge extraction, knowledge fusion, and knowledge reasoning using deep learning. At the same time, according to the research trend in recent years, the paper highlights and summarizes the latest research results on the integration of graph deep learning and knowledge reasoning applicable to graph data feature inference. Finally, an overview and technical outlook are made on the integration and development of deep learning and knowledge graphs, providing reference and ideas for future research directions.

Key words: knowledge graph, deep learning, knowledge graph construction, knowledge reasoning, graph deep learning

摘要： 知识图谱作为自然语言处理领域的一种结构化知识表示形式，能够描述现实世界中的概念及相互关系，常被应用于信息检索、数据管理等领域。深度学习因其具有自动学习多元数据内在规律和表示层次的特性，可用于大规模、高质量知识图谱的精准构建及有效推理，已逐渐成为新兴研究热点。为进一步促进深度学习和知识图谱的技术融合，以知识图谱构建及推理过程为主线，全面介绍深度学习在知识表示、知识抽取、知识融合、知识推理领域的相关理论及最新研究成果；同时，根据近年来的研究趋势，重点归纳与总结了适用于图数据特征推理的图深度学习与知识推理相融合的最新研究成果。最后，对深度学习和知识图谱的融合发展做了概要总结和技术展望，为未来研究发展提供参考和思路。

关键词: 知识图谱, 深度学习, 知识图谱构建, 知识推理, 图深度学习

SUN Yu, LIU Chuan, ZHOU Yang. Applications of Deep Learning in Knowledge Graph Construction and Reasoning[J]. Computer Engineering and Applications, 2025, 61(6): 36-52.

孙宇, 刘川, 周扬. 深度学习在知识图谱构建及推理中的应用[J]. 计算机工程与应用, 2025, 61(6): 36-52.

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