| 摘要: |
| 知识图谱作为近年来人工智能领域的一大热点研究方向, 已应用于现实中多个领域. 但是随着知识图谱应用场景日益多样化, 人们逐渐发现不随着时间改变而更新的静态知识图谱不能完全适应知识高频更新的场景. 为此, 研究者们提出时序知识图谱的概念, 一种包含时间信息的知识图谱. 对现有所有时序知识图谱表示与推理模型进行整理, 并归纳和建立一个表示与推理模型理论框架. 然后基于此对当前时序表示推理研究进展进行简要介绍分析和未来趋势预测, 以期望帮助研究者开发设计出更为优异的模型. |
| 关键词: 知识图谱 时序知识图谱 表示学习 知识推理 R-GCN |
| DOI：10.13328/j.cnki.jos.007093 |
| 分类号: |
| 基金项目:国家重点研发计划(2022YFB3103600); 国家自然科学基金(62272469); 长江学者奖励计划(Q2020245)陈子阳与王俞涵有同等贡献. |
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| Research Progress and Trend of Temporal Knowledge Graph Representation and Reasoning |
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WANG Yu-Han1, CHEN Zi-Yang2, ZHAO Xiang2, TAN Zhen1, XIAO Wei-Dong1, CHENG Xue-Qi3
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1.Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China;2.College of Systems Engineering, National University of Defense Technology, Changsha 410073, China;3.Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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| Abstract: |
| As a research hotspot in artificial intelligence in recent years, knowledge graphs have been applied to many fields in reality. However, with the increasingly diversified application scenarios of knowledge graphs, people gradually find that static knowledge graphs which do not change with time cannot fully adapt to the scenarios of high-frequency knowledge update. To this end, researchers propose the concept of temporal knowledge graphs containing temporal information. This study organizes all existing temporal knowledge graph representation and reasoning models and summarizes and constructs a theoretical framework for these models. Then, on this basis, it briefly introduces and analyzes the current research progress of temporal representation reasoning, and carries out the future trend prediction to help researchers develop and design better models. |
| Key words: knowledge graph temporal knowledge graph representation learning knowledge reasoning R-GCN |
