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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 6

Time-series knowledge graph completion method combining local-global historical pattern and historical knowledge frequency

Jia Kai1,2
Wang Yangping1,2
Yang Jingyu1,2
Zhang Xiquan1,2
1. School of Electronic & Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
2. National Virtual Simulation Experimental Teaching Center of Rail Transit Information & Control, Lanzhou 730070, China
DOI: 10.19734/j.issn.1001-3695.2024.10.0463

Abstract

Temporal knowledge graphs (TKGs) are dynamic representations of evolving facts, and their completion task involves predicting future unknown facts based on historical data. The key lies in understanding historical data. However, existing models have limitations in capturing the features of historical events and cannot accurately extract useful information from timestamps. From the perspective of historical evolution, considering the sequence, frequency, and periodic patterns of historical facts comprehensively is beneficial for predicting future facts. Therefore, this paper proposes a temporal knowledge graph completion algorithm (LGH-HKF) that integrates local-global historical patterns and historical knowledge frequency. Specifically, it first uses a local recurrent graph encoder network to model the intrinsic associations and dynamic evolution of events at adjacent timestamps; then, it uses a global historical encoder network to consider all relevant facts at previous timestamps to avoid losing entities or relations that do not appear at adjacent timestamps; next, it learns the frequency scores of these facts through a historical knowledge frequency learning module to enrich the model's prediction basis; finally, after balancing between the two encoders, we use a periodic decoder to perform inference and completion. The paper uses four benchmark datasets to evaluate the proposed method, and the experimental results prove that LGH-HKF is highly competitive compared to other current models in most cases.

Foundation Support

国家自然科学基金项目(62067006,62367005)
甘肃省知识产权计划项目(21ZSCQ013)
2024中央引导地方科技发展资金项目(332140068864)
甘肃省高校科研创新平台重大培育项目(2024CXPT-17)
甘肃省教育科技创新项目(2021jyjbgs-05)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.10.0463
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 6

Publish History

[2025-03-10] Accepted Paper

Cite This Article

贾凯, 王阳萍, 杨景玉, 等. 融合局部-全局历史模式与历史知识频率的时序知识图谱补全方法 [J]. 计算机应用研究, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0463. (Jia Kai, Wang Yangping, Yang Jingyu, et al. Time-series knowledge graph completion method combining local-global historical pattern and historical knowledge frequency [J]. Application Research of Computers, 2025, 42 (6). (2025-03-10). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0463. )

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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