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<p>Since the static Knowledge Graph cannot meet the dynamics of knowledge in the real world, Temporal Knowledge Graph has become a potential method for processing temporal knowledge. However, Temporal Knowledge Graph still faces the problem of incompleteness. Hence knowledge completion has become the main reasoning task for computing the missing facts in Knowledge Graph. In order to capture more richer historical information for predicting future events, we propose a new event forecasting model called Global-Recent Historical Network (GRHNet). In GRHNet, we use statistical methods to simulate the evolution of events. Furthermore, we design a global history learner to capture the repeatability of knowledge, and a recent history learner to capture time-variability of knowledge. GRHNet is evaluated on two benchmark datasets. The results demonstrate that, compared with state-of-the-art baselines, GRHNet achieves at least a 3% relative improvement in MRR.</p>

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Temporal knowledge graph reasoning using global and recent history information

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

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  • Changlong Wang,
  • Jianlong Cao,
  • Wenzheng Guo,
  • Jie Hu,
  • Yaoyao Hu,
  • Yi Liu,
  • Yawei Li,
  • Huimei Tian,
  • Pengjuan Lu,
  • Wenhao Zhao,
  • Zhixiong Yin

摘要

Since the static Knowledge Graph cannot meet the dynamics of knowledge in the real world, Temporal Knowledge Graph has become a potential method for processing temporal knowledge. However, Temporal Knowledge Graph still faces the problem of incompleteness. Hence knowledge completion has become the main reasoning task for computing the missing facts in Knowledge Graph. In order to capture more richer historical information for predicting future events, we propose a new event forecasting model called Global-Recent Historical Network (GRHNet). In GRHNet, we use statistical methods to simulate the evolution of events. Furthermore, we design a global history learner to capture the repeatability of knowledge, and a recent history learner to capture time-variability of knowledge. GRHNet is evaluated on two benchmark datasets. The results demonstrate that, compared with state-of-the-art baselines, GRHNet achieves at least a 3% relative improvement in MRR.