##基于动态图神经网络的知识图谱推理

Abstract

This paper introduces a novel approach to knowledge graph reasoning based on Dynamic Graph Neural Networks (DGNNs). Traditional knowledge graph reasoning models often treat knowledge graphs as static structures, failing to capture the inherent dynamic evolution of entities and relationships. This work addresses this limitation by leveraging DGNNs to model these dynamic changes. The core mechanism involves utilizing DGNNs to learn the dynamic patterns within the knowledge graph and employing graph convolutional operations for reasoning. The proposed method demonstrates improved accuracy and robustness compared to static knowledge graph reasoning models. We present a theoretical framework and outline the architecture of the proposed DGNN-based knowledge graph reasoning system. The key contribution lies in the dynamic modeling capability, enabling the system to adapt to evolving knowledge and perform more reliable inferences. We explore the impact of different DGNN architectures and training strategies on the performance of the system. The results presented suggest that dynamic graph neural networks offer a promising direction for advancing knowledge graph reasoning.