Hierarchical graph attention network for temporal knowledge graph reasoning

doi:10.1016/j.neucom.2023.126390

	Hierarchical graph attention network for temporal knowledge graph reasoning
	Shao, Pengpeng1 ; He, Jiayi 1; Li, Guanjun1 ; Zhang, Dawei1 ; Tao, Jianhua2
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2023-09-14
卷号	550 页码:8
通讯作者	Tao, Jianhua(jhtao@tsinghua.edu.cn)
摘要	Temporal knowledge graphs (TKGs) reasoning has attracted increasing research interest in recent years. However, most of the existing TKGs reasoning models aim to learn a dynamic entity representation by binding timestamps information with the entities, neglecting to learn adaptive entity representation that is valuable to the query from relevant historical facts. To this end, we propose a Hierarchical Graph Attention neTwork (HGAT) for the TKGs reasoning task. Specifically, we design a hierarchical neighbor encoder to model the time-oriented and task-oriented roles of the entities. The time-aware mechanism is developed in the first layer to differentiate the contributions of query-relevant historical facts at different timestamps to the query. The designed relation-aware attention is used in the second layer to discern the contributions of the structural neighbors of an entity. Through this hierarchical encoder, our model can absorb valuable knowledge effectively from the relevant historical facts, and thus learn more expressive adaptive entity representation for the query. Finally, we evaluate our model performance on four TKGs datasets and justify its superiority against vaerious state-of-the-art baselines. & COPY; 2023 Elsevier B.V. All rights reserved.
关键词	Temporal knowledge graphs Graph attention network Reasoning
DOI	10.1016/j.neucom.2023.126390
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)[61831022] ; National Natural Science Foundation of China (NSFC)[62276259] ; National Natural Science Foundation of China (NSFC)[62201572] ; National Natural Science Foundation of China (NSFC)[62206278] ; National Natural Science Foundation of China (NSFC)[U21B2010] ; Beijing Municipal Science amp; Technology Commission, Administrative Commission of Zhongguancun Science Park[Z211100004821013] ; CCF-Baidu Open Fund[OF2022025]
项目资助者	National Natural Science Foundation of China (NSFC) ; Beijing Municipal Science amp; Technology Commission, Administrative Commission of Zhongguancun Science Park ; CCF-Baidu Open Fund
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001038714700001
出版者	ELSEVIER
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53844
专题	多模态人工智能系统全国重点实验室
通讯作者	Tao, Jianhua
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing, Peoples R China 2.Tsinghua Univ, Dept Automat, Beijing, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Shao, Pengpeng,He, Jiayi,Li, Guanjun,et al. Hierarchical graph attention network for temporal knowledge graph reasoning[J]. NEUROCOMPUTING,2023,550:8.
APA	Shao, Pengpeng,He, Jiayi,Li, Guanjun,Zhang, Dawei,&Tao, Jianhua.(2023).Hierarchical graph attention network for temporal knowledge graph reasoning.NEUROCOMPUTING,550,8.
MLA	Shao, Pengpeng,et al."Hierarchical graph attention network for temporal knowledge graph reasoning".NEUROCOMPUTING 550(2023):8.