Directed Knowledge Graph Reasoning Model Based on Graph Attention Mechanism

This article proposes a directed knowledge graph inference model based on graph attention mechanism, aiming to solve the accuracy and efficiency problems in the knowledge graph inference process. Knowledge graph inference, as an important component of improving the quality of knowledge graphs, aims to explore potential entity relationships and complete the knowledge graph. However, traditional reasoning methods have limited effectiveness in handling large-scale knowledge graphs, especially in the context of increasing data volume and complex relationships, making existing methods inadequate. Therefore, this study introduces a model structure combining RED-GNN and GATv2, which improves the accuracy and efficiency of inference by dynamically capturing the structural information and global features of neighboring nodes. In addition, this chapter provides a detailed description of the various components of the model, including the recursive module, inference module, and evaluation module. Empirical experiments were conducted on multiple knowledge graph datasets to validate the effectiveness of the proposed model. The experimental results show that this model exhibits significant advantages in complex relational reasoning tasks, effectively balancing the accuracy and efficiency of reasoning, and providing new ideas and methods for the development of knowledge graph applications.