时序知识图谱表示学习

doi:10.11896/jsjkx.220500204

计算机科学 ›› 2022, Vol. 49 ›› Issue (9): 162-171.doi: 10.11896/jsjkx.220500204

时序知识图谱表示学习

徐涌鑫^1,2, 赵俊峰^1,2,3, 王亚沙^1,2,3, 谢冰^1,2,3, 杨恺^1,2,3

1 北京大学计算机学院北京 100871
2 高可信软件技术教育部重点实验室北京 100871
3 北京大学(天津滨海)新一代信息技术研究院天津 300450

收稿日期:2021-10-22 修回日期:2022-05-16 出版日期:2022-09-15 发布日期:2022-09-09
通讯作者: 赵俊峰(zhaojf@pku.edu.cn)
作者简介:(xuyx@stu.pku.edu.cn)
基金资助:
国家自然科学基金(62172011)

Temporal Knowledge Graph Representation Learning

XU Yong-xin^1,2, ZHAO Jun-feng^1,2,3, WANG Ya-sha^1,2,3, XIE Bing^1,2,3, YANG Kai^1,2,3

1 School of Computer Science,Peking University,Beijing 100871,China
2 Key Laboratory of High Confidence Software Technologies,Ministry of Education,Beijing 100871,China
3 Peking University Information Technology Institute(Tianjin Binhai),Tianjin 300450,China

Received:2021-10-22 Revised:2022-05-16 Online:2022-09-15 Published:2022-09-09
About author:XU Yong-xin,born in 1998,postgraduate.His main research interests include knowledge graph and so on.
ZHAO Jun-feng,born in 1974,Ph.D,research professor,is a member of China Computer Federation.Her main research interests include big data analysis,knowledge graph,urban computing and so on.
Supported by:
National Natural Science Foundation of China(62172011).

摘要/Abstract

摘要： 知识图谱作为一种结构化的人类知识形式,对海量多源异构异质的数据语义互通起到了很好的支撑作用,并有效地支持了数据分析等任务,成为了近年来学术界和工业界的研究热点。目前大多数知识图谱都是根据非实时的静态数据构建,没有考虑实体和关系的时间特性。然而社交网络通信、金融贸易、疫情传播网络等应用场景的数据具有实时动态的特点以及复杂的时间特性,如何利用时序数据构建知识图谱并且对该知识图谱进行有效建模是一个具有挑战性的问题。目前,有许多研究工作利用时序数据中的时间信息丰富知识图谱的特征,赋予知识图谱动态特征,将事实三元组拓展为(头实体,关系,尾实体,时间)的四元组表示,使用时间相关四元组进行知识表示的知识图谱被统称为时序知识图谱。文中对时序知识图谱相关文献进行整理和分析,并对时序知识图谱表示学习的工作进行了全面综述。具体地,首先简单介绍了时序知识图谱的背景与定义;其次总结了时序知识图谱表示学习方法相比传统知识图谱表示学习方法的优点;然后从事实的建模方法角度详细阐述了时序知识图谱表示学习的主要方法,并且介绍了上述方法使用到的数据集;最后对该技术的主要挑战进行了总结,并对其未来研究方向进行了展望。

关键词: 知识图谱, 深度学习, 表示学习, 时间信息, 动态过程

Abstract: As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the attention of academia and industry.At present,most knowledge graphs are constructed based on non-real-time static data,without considering the temporal characteristics of entities and relationships.However,data in application scenarios such as social network communication,financial trade,and epidemic spreading network are highly dynamic and exhibit complex temporal properties.How to use time series data to build knowledge graphs and effectively model them is a challenging problem.Recently,numerous studies use temporal information in time series data to enrich the characteristics of knowledge graphs,endowing know-ledge graphs with dynamic features,expanding fact triples into quadruple representation(head entity,relationship,tail entity,time).The knowledge graph which utilizes time-related quadruples to represent knowledge are collectively referred to as temporal knowledge graph.This paper summarizes the research work of temporal knowledge graph representation learning by sorting out and analyzing the corresponding literature.Specifically,it first briefly introduce the background and definition of temporal know-ledge graph.Next,it summarizes the advantages of the temporal knowledge graph representation learning method compared with the traditional knowledge graph representation learning method.Then it elaborates on the recent method of temporal knowledge graph representation learning from the perspective of the method modeling facts,introduces the dataset used by the above method and summarizes the main challenges of this technology.Finally,the future research direction is prospected.

Key words: Knowledge graph, Deep learning, Representation learning, Temporal information, Dynamic process

中图分类号:

TP311

徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习[J]. 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204

XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning[J]. Computer Science, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204

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时序知识图谱表示学习

Temporal Knowledge Graph Representation Learning

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