知识图谱嵌入方法的链接预测研究综述

doi:10.3778/j.issn.1002-8331.2407-0158

摘要/Abstract

摘要： 知识图谱中普遍存在实体和关系缺失等不足，知识图谱补全能够有效解决上述不足被研究者广泛关注。知识图谱嵌入方法的链接预测作为知识补全的重要研究方向，能够预测出知识图谱中缺失的实体或关系，来补全知识图谱并增强其完整性。阐述了知识图谱链接预测的研究背景、意义和定义；以嵌入单位的实体个数为分类标准，将知识图谱嵌入的链接预测模型划分为双实体嵌入链接预测模型和多实体嵌入链接预测模型，详细阐述模型构建思路，分析实验结果并总结各类模型优缺点。最后，展望知识图谱嵌入链接预测现状以及未来研究方向，为后续的发展提供启示和指导。

关键词: 知识图谱, 知识图谱嵌入, 链接预测, 知识图谱补全

Abstract: Knowledge graphs often suffer from issues such as missing entities and relationships. Knowledge graph completion, which addresses these deficiencies, has garnered significant attention from researchers. Link prediction based on knowledge graph embedding, as an important research direction for knowledge graph completion, can predict missing entities or relationships in the knowledge graph, thereby enhancing its completeness. Firstly, this paper expounds the research background, significance and definition of link prediction in knowledge graph. Secondly, based on the number of entities in the embedding unit, the link prediction models for knowledge graph embedding are divided into two-entity embedding link prediction models and multi-entity embedding link prediction models. The idea of model construction is elaborated, the experimental results are analyzed, and the advantages and disadvantages of various models are summarized. Finally, the current status and future research directions of knowledge graph embedded link prediction are prospected to provide inspiration and guidance for subsequent development.

Key words: knowledge graph, knowledge graph embedding, link prediction, knowledge graph completion

刘海超, 柳林, 王海龙, 赵巍伟, 刘静. 知识图谱嵌入方法的链接预测研究综述[J]. 计算机工程与应用, 2025, 61(8): 17-34.

LIU Haichao, LIU Lin, WANG Hailong, ZHAO Weiwei, LIU Jing. Survey of Link Prediction in Knowledge Graph Embedding Methods[J]. Computer Engineering and Applications, 2025, 61(8): 17-34.

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