Tucker decomposition-based temporal knowledge graph completion
文献类型：期刊论文
| 作者 | Shao, Pengpeng1; Zhang, Dawei1 ; Yang, Guohua1; Tao, Jianhua1,2,3; Che, Feihu1; Liu, Tong1
|
| 刊名 | KNOWLEDGE-BASED SYSTEMS
 |
| 出版日期 | 2022-02-28 |
| 卷号 | 238页码:9 |
| 关键词 | Temporal knowledge graphs Tucker decomposition Reconstruction Contrastive learning |
| ISSN号 | 0950-7051 |
| DOI | 10.1016/j.knosys.2021.107841 |
| 通讯作者 | Tao, Jianhua(jhtao@nlpr.ia.ac.cn) |
| 英文摘要 | Knowledge 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. |
| 资助项目 | National 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] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000779180700014 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research & Development Plan of China ; National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48252]  |
| 专题 | 模式识别国家重点实验室_智能交互 |
| 通讯作者 | Tao, Jianhua |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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