Enhanced Knowledge Graph Completion Based on Structure-Aware and Semantic Fusion Driven by Large Language Models

Knowledge graphs (KGs) have emerged as fundamental infrastructures for organizing structured information across a wide range of AI applications. Practically, KGs are often incomplete, which limits their effectiveness. Knowledge Graph Completion (KGC) has become a critical research problem. Existing methods of KGC primarily rely on graph structure or textual descriptions independently, often failing to capture the complex interplay between structural topology and rich semantic context. Recent advances in Large Language Models (LLMs) offer promising capabilities in understanding and generating human-like semantic representations. However, effectively integrating such models with structured graph information remains a challenging and underexplored area. In this work, we propose an enhanced KGC framework that leverages a structure-aware and semantic fusion mechanisms driven by the representational power of LLMs. Our method jointly encodes the topological structure of the graph and the textual semantics of entities and relations, allowing for more informed and context-rich KGC. The experimental results of benchmark datasets demonstrate that our approach outperforms existing baselines, particularly in scenarios with sparse graph connectivity or limited textual information. In particular, on the WN18RR dataset, the model demonstrates a 12.4% increase in Hits@3 and an 11.7% increase in Hits@10.