Computer Science ›› 2022, Vol. 49 ›› Issue (9): 162-171.doi: 10.11896/jsjkx.220500204

• Artificial Intelligence • Previous Articles     Next Articles

Temporal Knowledge Graph Representation Learning

XU Yong-xin1,2, ZHAO Jun-feng1,2,3, WANG Ya-sha1,2,3, XIE Bing1,2,3, YANG Kai1,2,3   

  1. 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).

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

CLC Number: 

  • TP311
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