Research on Temporal Knowledge Graph Completion Method for Emergent Events Based on BiGRU and Graph Contrastive Learning

Authors

• Peng WU, School of Intelligent Manufacturing, Nanjing University of Science & Technology, Nanjing 210094
• Zhenyu LU, School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094
• Xuechen ZHANG, School of Cyber Science & Engineering, Nanjing University of Science & Technology, Nanjing 210094

Keywords

temporal knowledge graph; temporal knowledge graph construction; temporal knowledge graph completion; graph contrastive learning; sudden events

Abstract

[Purpose/significance] During emergencies, social media short texts contain critical information but are heavily interfered with by noise. Traditional static knowledge graph completion techniques struggle to effectively address their dynamic evolution and data sparsity issues, making it imperative to introduce temporal modeling methods. [Method/process] This study proposes a dynamic completion framework that combines the temporal feature capture capability of Bidirectional Gated Recurrent Units (BiGRU) with the noise-resistant representation learning advantages of Graph Contrastive Learning (GCL). At the completion level, the ConBiTE method is introduced, which captures temporal dependencies through self-attention mechanisms and BiGRU, while leveraging GCL to enhance the completion of missing entities and relationships. At the construction level, RoBERTa-CNN-BiLSTM-CRF is employed for entity recognition, and the Wenxin large language model is utilized for relationship extraction, thereby improving the quality and efficiency of graph construction. [Result/conclusion] Experiments demonstrate that the proposed method outperforms traditional approaches in both completion and construction tasks, providing comprehensive technical support for dynamic information analysis and emergency response during emergencies, with significant theoretical and practical implications.

First Page

1

Last Page

11

Submission Date

May 2025

Revision Date

October 2025

Acceptance Date

October 2025

Publication Date

January 2026

Digital Object Identifier (DOI)

ꎺ 10.19809/j.cnki.kjqbyj.2026.01.001

Recommended Citation

WU, Peng; LU, Zhenyu; and ZHANG, Xuechen (2026) "Research on Temporal Knowledge Graph Completion Method for Emergent Events Based on BiGRU and Graph Contrastive Learning," Journal of Scientific Information Research: Vol. 8: Iss. 1, Article 1.
DOI: ꎺ 10.19809/j.cnki.kjqbyj.2026.01.001
Available at: https://eng.kjqbyj.com/journal/vol8/iss1/1

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