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Tucker decomposition-based temporal knowledge graph completion
Shao, Pengpeng1; Zhang, Dawei1; Yang, Guohua1; Tao, Jianhua1,2,3; Che, Feihu1; Liu, Tong1
Source PublicationKNOWLEDGE-BASED SYSTEMS
ISSN0950-7051
2022-02-28
Volume238Pages:9
Corresponding AuthorTao, Jianhua(jhtao@nlpr.ia.ac.cn)
AbstractKnowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the knowledge graphs. To enrich the existing knowledge graphs, recent years have witnessed that many algorithms for link prediction and knowledge graphs embedding have been designed to infer new facts. But most of these studies focus on the static knowledge graphs and ignore the temporal information which reflects the validity of knowledge. Developing the model for temporal knowledge graphs completion is an increasingly important task. In this paper, we build a new tensor decomposition model for temporal knowledge graphs completion inspired by the Tucker decomposition of order-4 tensor. Furthermore, to further improve the basic model performance, we provide three kinds of methods including cosine similarity, contrastive learning, and reconstruction-based to incorporate the prior knowledge into the proposed model. Because the core tensor contains a large number of parameters on the proposed model, thus we present two embedding regularization schemes to avoid the over-fitting problem. By combining these two kinds of regularization with the proposed model, our model outperforms baselines with an explicit margin on three temporal datasets (i.e. ICEWS2014, ICEWS05-15, GDELT).& nbsp;(c) 2021 Published by Elsevier B.V.
KeywordTemporal knowledge graphs Tucker decomposition Reconstruction Contrastive learning
DOI10.1016/j.knosys.2021.107841
Indexed BySCI
Language英语
Funding ProjectNational Key Research & Development Plan of China[2017YFC0820602] ; National Natural Science Foundation of China (NSFC)[61831022] ; National Natural Science Foundation of China (NSFC)[61771472] ; National Natural Science Foundation of China (NSFC)[61773379] ; National Natural Science Foundation of China (NSFC)[61901473]
Funding OrganizationNational Key Research & Development Plan of China ; National Natural Science Foundation of China (NSFC)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000779180700014
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48253
Collection模式识别国家重点实验室_智能交互
Corresponding AuthorTao, Jianhua
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Shao, Pengpeng,Zhang, Dawei,Yang, Guohua,et al. Tucker decomposition-based temporal knowledge graph completion[J]. KNOWLEDGE-BASED SYSTEMS,2022,238:9.
APA Shao, Pengpeng,Zhang, Dawei,Yang, Guohua,Tao, Jianhua,Che, Feihu,&Liu, Tong.(2022).Tucker decomposition-based temporal knowledge graph completion.KNOWLEDGE-BASED SYSTEMS,238,9.
MLA Shao, Pengpeng,et al."Tucker decomposition-based temporal knowledge graph completion".KNOWLEDGE-BASED SYSTEMS 238(2022):9.
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