时态知识图谱表示学习研究综述

doi:10.3778/j.issn.1002-8331.2409-0253

摘要/Abstract

摘要： 知识图谱表示学习旨在将知识图谱中的符号化表示形式转换成数值化表示形式，更好地服务于知识驱动型应用。时态知识图谱表示学习技术充分利用知识图谱中的时间信息，取得了显著的性能提升。对时态知识图谱表示学习方法进行了系统性的综述，主要从四个方面进行：（1）简要介绍时态知识图谱表示学习的相关概念、典型任务和传统的静态方法；（2）总结了时态知识图谱表示学习的两大类方法，即面向内插任务的方法和面向外推任务的方法，分别介绍两类方法中的典型模型；（3）梳理了8个用于时态知识图谱表示学习的基准数据集和若干代表性模型在基准数据集上的评测结果；（4）分析了当前面临的技术挑战以及其中蕴含的机会。

关键词: 知识图谱（KG）, 时态知识图谱（TKG）, 表示学习, 知识表示

Abstract: Knowledge graph (KG) representation learning aims to convert the original symbolic knowledge representation into numerical knowledge representation for better knowledge-driven applications. Temporal knowledge graph (TKG) representation learning technology makes full use of time information in KG and achieves performance improvement. This paper systematically reviews TKG representation learning methods from four aspects: (1) It briefly introduces the related concepts, typical tasks of TKG representation learning and traditional static methods. (2) It summarizes two kinds of methods of TKG representation learning, namely, interpolation task oriented method and extrapolation task oriented method, and introduces the typical models of the two kinds of methods respectively. (3) Eight benchmark datasets for TKG representation learning and evaluation results of several typical models on the benchmark datasets are sorted out. (4) The current technical challenges and opportunities are analyzed.

Key words: knowledge graph (KG), temporal knowledge graph (TKG), representation learning, knowledge representation

何鹏, 姚瑶, 刘秋菊. 时态知识图谱表示学习研究综述[J]. 计算机工程与应用, 2025, 61(14): 37-53.

HE Peng, YAO Yao, LIU Qiuju. Survey on Temporal Knowledge Graph Representation Learning[J]. Computer Engineering and Applications, 2025, 61(14): 37-53.

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