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Open Access

ARTICLE

Dynamic Knowledge Graph Reasoning Based on Distributed Representation Learning

Qiuru Fu1, Shumao Zhang1, Shuang Zhou1, Jie Xu1,*, Changming Zhao2, Shanchao Li3, Du Xu1,*
1 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
2 School of Computer Science, Chengdu University of Information Technology, Chengdu, 610103, China
3 Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, USA
* Corresponding Author: Jie Xu. Email: email; Du Xu. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.070493

Received 17 July 2025; Accepted 01 October 2025; Published online 30 October 2025

Abstract

Knowledge graphs often suffer from sparsity and incompleteness. Knowledge graph reasoning is an effective way to address these issues. Unlike static knowledge graph reasoning, which is invariant over time, dynamic knowledge graph reasoning is more challenging due to its temporal nature. In essence, within each time step in a dynamic knowledge graph, there exists structural dependencies among entities and relations, whereas between adjacent time steps, there exists temporal continuity. Based on these structural and temporal characteristics, we propose a model named “DKGR-DR” to learn distributed representations of entities and relations by combining recurrent neural networks and graph neural networks to capture structural dependencies and temporal continuity in DKGs. In addition, we construct a static attribute graph to represent entities’ inherent properties. DKGR-DR is capable of modeling both dynamic and static aspects of entities, enabling effective entity prediction and relation prediction. We conduct experiments on ICEWS05-15, ICEWS18, and ICEWS14 to demonstrate that DKGR-DR achieves competitive performance.

Keywords

Dynamic knowledge graph reasoning; recurrent neural network; graph convolutional network; graph attention mechanism

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