This paper presents Integrated Semantics-Structure Analysis in Knowledge Graph Completion (ISA-KGC), a new framework for Knowledge Graph Completion (KGC) aimed at addressing the incompleteness of knowledge graphs (KGs). ISA-KGC integrates Graph Neural Networks (GNN) with Transformer-based models, effectively blending structural and semantic information within Knowledge Graphs. This fusion enhances comprehension of KGs beyond what traditional methods offer. The framework utilizes Knowledge Graph Embedding (KGE) models, with GNN employed to augment these models, thus enhancing the overall analysis and interpretation of Knowledge Graphs. The effectiveness of ISA-KGC is validated through benchmark datasets FB15K-237 and WN18RR, showing notable improvements in performance metrics like hit@10 compared to existing methods.
