Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 6
1737-1741

Temporal knowledge graph prediction based on entities activity and copy generation

Liu Enhai1,2,3
Chu Hang1
Wang Liqin1,2,3
Dong Yongfeng1,2,3
1. School of Artificial Intelligence & Data Science, Hebei University of Technology, Tianjin 300401, China
2. Hebei Key Laboratory of Big Data Computing, Tianjin 300401, China
3. Hebei Engineering Research Center of Data-Driven Industrial Intelligent, Tianjin 300401, China
DOI: 10.19734/j.issn.1001-3695.2021.11.0611

Abstract

The existing temporal knowledge graph reasoning methods were mainly based on static knowledge graph reasoning methods. These methods utilized the structural features of knowledge graphs to mine potential semantic information and relationship features, ignoring the importance of entity temporal information. Therefore, this paper proposed an based entity activity and copy generation(EACG) temporal knowledge graph reasoning method. Firstly, this paper employed an improved graph convolutional network to model multi-relational entities, effectively mining the latent semantic information and structural features of knowledge graphs. Next, this paper utilized the temporal encoder to learn the temporal characteristics of entities based on the activity of entities. Finally, this paper used the copy generation mechanism to further learn the historical information of know-ledge graphs and improve the ability to model temporal data. The experimental results of reasoning on temporal knowledge graph datasets ICEWS14, ICEWS05-15, and GDELT show that EACG outperforms the sub-optimal method by 2%, 10% and 5% respectively in the MRR evaluation index.

Foundation Support

国家自然科学基金资助项目(61806072)
天津市自然科学基金资助项目(19JCZDJC40000)
河北省高等学校科学技术研究项目(QN2021213)
河北省自然科学基金资助项目(F2020202008)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0611
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 6
Section: Algorithm Research & Explore
Pages: 1737-1741
Serial Number: 1001-3695(2022)06-023-1737-05

Publish History

[2022-01-17] Accepted Paper
[2022-06-05] Printed Article

Cite This Article

刘恩海, 楚航, 王利琴, 等. 基于实体活跃度及复制生成的时序知识图谱推理 [J]. 计算机应用研究, 2022, 39 (6): 1737-1741. (Liu Enhai, Chu Hang, Wang Liqin, et al. Temporal knowledge graph prediction based on entities activity and copy generation [J]. Application Research of Computers, 2022, 39 (6): 1737-1741. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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