SeSKGC: A Semantic–Structural Fusion Framework for Knowledge Graph Completion

Knowledge graphs play a vital role in tasks such as recommendation systems, question-answering systems, and information retrieval. However, during practical construction, they commonly suffer from structural incompleteness and sparse relationships, which limit reasoning performance and downstream applications. Existing methods typically focus solely on either structural modeling or semantic modeling: embedding models relying solely on graph structures struggle to leverage textual information about entities and relationships. In contrast, semantic approaches relying solely on pre-trained language models struggle to accurately capture complex relationship patterns. To address this challenge, this paper proposes SeSKGC, a semantic–structural fusion knowledge graph completion model. At the semantic level, the model employs the DeBERTa pre-trained language model to encode entity and relation text. It incorporates a neighbor text augmentation mechanism to introduce local semantic context and enhance representation quality. At the structural level, it adopts complex-space rotation to model relationships using a RotatE-like approach, and aggregates local topological information through relative position attention to capture complex relationship patterns. At the scoring stage, the model employs a weighted fusion strategy to combine semantic and structural scores and utilizes InfoNCE contrastive loss for joint optimization. Experiments conducted on WN18RR and FB15k-237 datasets demonstrate that SeSKGC achieves overall superior performance on metrics including MRR and Hits@N compared to multiple representative baseline methods. Ablation studies and parameter sensitivity analysis of fusion weight λ further reveal that the semantic encoding and structural modeling modules exhibit distinct complementary roles, while the weighted fusion design in the scoring layer plays a crucial role in enhancing model performance and stability.