时态知识图谱补全的方法及其进展

申宇铭; 杜剑峰

doi:10.11959/j.issn.2096-0271.2021024

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时态知识图谱补全的方法及其进展

专题：基于大数据的知识图谱及其应用 | 更新时间：2024-06-03

- 时态知识图谱补全的方法及其进展
- Temporal knowledge graph completion:methods and progress
- 大数据 2021年7卷第3期页码：2021024
- 作者机构：
- 作者简介：
  
  [ "申宇铭（1976- ），男，博士，广东外语外贸大学教授，主要研究方向为知识表示与推理、知识图谱。主持或参与多项国家自然科学基金和省部级项目。近年来在《计算机学报》《软件学报》等国内重要期刊，以及国际重要期刊和国际会议上发表论文20余篇。担任CCKS、AAAI、EMNLP等国内外重要学术会议的程序委员会委员。" ]
  [ "杜剑峰（1976- ），男，博士，广东外语外贸大学教授，中国中文信息学会语言与知识计算专业委员会委员，主要研究方向为知识表示与推理、数据挖掘和自然语言处理。在AAAI、WWW、ISWC、CIKM和KAIS等学术会议上发表数十篇文章，获得多项国家自然科学基金项目资助。担任Journal of Web Semantics编委，长期担任CCKS、CSWS、IJCAI、AAAI、ISWC、JIST等学术会议的程序委员会成员，曾担任CSWS 2014程序委员会主席。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61876204);广东省自然科学基金资助项目;Guangdong Natural Science Foundation(2018A030313777)
- DOI：10.11959/j.issn.2096-0271.2021024
  中图分类号： TP18
- 网络首发：2021-05，
  
  纸质出版：2021-05-15
- 稿件说明：
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申宇铭, 杜剑峰. 时态知识图谱补全的方法及其进展[J]. 大数据, 2021,7(3):2021024.

Yuming SHEN, Jianfeng DU. Temporal knowledge graph completion:methods and progress[J]. Big data research, 2021, 7(3): 2021024.
申宇铭, 杜剑峰. 时态知识图谱补全的方法及其进展[J]. 大数据, 2021,7(3):2021024. DOI： 10.11959/j.issn.2096-0271.2021024.

Yuming SHEN, Jianfeng DU. Temporal knowledge graph completion:methods and progress[J]. Big data research, 2021, 7(3): 2021024. DOI： 10.11959/j.issn.2096-0271.2021024.

摘要

时态知识图谱是将时间信息添加到传统的知识图谱而得到的。近年来，时态知识图谱补全受到了学术界的高度关注，并成为研究热点之一。总结了目前时态知识图谱补全的两大类方法，即基于符号逻辑的方法和基于知识表示学习的方法，比较分析了两类方法的优缺点，展望了未来时态补全方法的发展方向，还总结了7个用于时态知识图谱补全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。

Abstract

Temporal knowledge graph (TKG) are obtained by adding the time information of real-world knowledge to classical knowledge graphs.Recently

TKG completion has drawn much attention and become a hot topic in research.Two main methodologies for TKG completion were summarized

one based on symbolic logic whereas and the other based on knowledge representation learning.The pros and cons of these two different methodologies were discussed

highlighting some directions for enhancing TKG completion in future research.Also

seven benchmark datasets for TKG completion and evaluation results of several typical models on the benchmark datasets were introduced.

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references

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