Adaptive Knowledge Distillation with GCN–GAT Teacher–Student Models for Link Prediction on out of Entity Triples

Link prediction fails on triples with unseen entities due to sparse representations from missing embeddings. To address this, GGTSM is proposed, an adaptive knowledge distillation framework using a hybrid Graph Convolutional Network - Graph Attention Network architecture for teacher and student models. GGTSM predicts external entities based on known triple structure and relationships. The teacher network learns entity and relation embeddings via supervised training. An adaptive weighting mechanism, using relations and node attention scores generated by teacher, calculates distillation loss weights to guide the student in learning critical graph patterns. During reasoning for triples containing unseen entities, the student performs guided fine-tuning using output of teacher, without external text features. GGTSM employs subgraph analysis to integrate batch-level knowledge, initializing embeddings from adjacent nodes and relations of input triples. Experiments on FB15k_237, WN18RR, and NELL-995 show GGTSM achieves significant gains: an average 0.15 improvement in Mean Reciprocal Rank, and average improvements of 18% (Hits@1), 41% (Hits@3), and 63% (Hits@10) over the baseline. These results confirm adaptive knowledge distillation reduces representation sparsity for unseen entities, enhances generalization, and provides a flexible paradigm for dynamic knowledge graph reasoning.