Two-Stage Knowledge Graph Completion Based on Semantic Features and High-Order Structural Features.

Recently, multi-head Graph Attention Networks (GATs) have incorporated attention mechanisms to generate more enriched feature embeddings, demonstrating significant potential in Knowledge Graph Completion (KGC) tasks. However, existing GATs based KGC approaches struggle to update entities with few neighbors, making it challenging to obtain structured semantic information and overlooking complex and implicit information in distant triples. To this effect, we propose a novel model named the Two-Stage KGC model with integrated High-Order Structural Features (HOSAT), designed to enhance the learning process of GATs. Initially, we leverage the conventional GATs module to acquire embeddings encapsulating local semantic intricacies. Subsequently, we introduce a global biased random walk algorithm, strategically amalgamating graph topology, entity attributes, and relationship attributes. This algorithm aims to extract high-order structured semantic neighbor sequences from multiple perspectives and construct nuanced reasoning paths. By propagating the embedding along this path, it is ensured that with an increasing number of iterations, the aggregated information of each node becomes an almost perfect combination of local and global features. Evaluation on two public benchmark datasets using entity prediction methods demonstrates that HOSAT achieves substantial performance improvements over state-of-the-art methods.