| YU Yong ( 余 泳)* **,CHEN Shudong* **,TONG Da* **,QI Donglin* **,PENG Fei* **,ZHAO Hua***.[J].高技术通讯(英文),2023,29(4):348~357 |
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| RotatS: temporal knowledge graph completion based on rotation and scaling in 3D space |
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| DOI： |
| 中文关键词: |
| 英文关键词: knowledge graph(KG),temporal knowledge graph(TKG),knowledge graph completion(
KGC),rotation and scaling (RotatS) |
| 基金项目: |
| Author Name | Affiliation | | YU Yong ( 余 泳)* ** | (*Institute of Microelectronics, Chinese Academy of Sciences,Beijing 100191,P. R. China)
(**University of Chinese Academy of Sciences,Beijing 100191,P. R. China)
(***Beijing Institute of Tracking and Telecommunications Technology,Beijing 100191,P. R. China) | | CHEN Shudong* ** | | | TONG Da* ** | | | QI Donglin* ** | | | PENG Fei* ** | | | ZHAO Hua*** | |
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| 中文摘要: |
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| 英文摘要: |
| As the research of knowledge graph(KG)is deepened and widely used,knowledge graph completion(
KGC)has attracted more and more attentions from researchers,especially in scenarios of intelligent
search,social networks and deep question and answer(Q&A). Current research mainly focuses
on the completion of static knowledge graphs,and the temporal information in temporal knowledge
graphs(TKGs)is ignored. However,the temporal information is definitely very helpful for the
completion. Note that existing researches on temporal knowledge graph completion are difficult to
process temporal information and to integrate entities,relations and time well. In this work,a rotation
and scaling (RotatS) model is proposed,which learns rotation and scaling transformations from head
entity embedding to tail entity embedding in 3D spaces to capture the information of time and relations
in the temporal knowledge graph. The performance of the proposed RotatS model have been
evaluated by comparison with several baselines under similar experimental conditions and space complexity
on four typical knowl good graph completion datasets publicly available online. The study
shows that RotatS can achieve good results in terms of prediction accuracy. |
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