Constructing the Relational Graph Attention Network Framework for Medical Knowledge Graph Reasoning

Accurately mining latent associations among biomedical entities is a cornerstone of progress in precision medicine and drug discovery. Knowledge graphs (KGs) provide structured support for this endeavor, with KG reasoning serving as a key enabling technology. Traditional graph models struggle to accommodate the inherent heterogeneity and data sparsity of biomedical KGs, making heterogeneous graph convolutional networks a preferred methodological path due to their inherent design for such data. This study refines the Relational Graph Attention Network(RGAT) by constructing multi-dimensionally enhanced node features and devising a hierarchical relation-aware attention mechanism. The training process is bolstered by strategies including hard negative mining, gradient accumulation, and a hybrid loss function, complemented by optimization techniques such as early stopping and adaptive learning rate scheduling. The model generates attention weights based on both node features and relation types, aggregating multi-source contextual dependencies through a multi-head attention mechanism to simultaneously perform entity link prediction and precise relation type identification. Experiments focus on the RGAT model's performance in reasoning over biomedical KGs, validating its effectiveness for link prediction tasks in the biomedical domain. This work furnishes a more diverse set of technical methodologies for mining biomedical entity associations, drug repurposing, and drug-target discovery.