SRE-KGC : A Knowledge Graph Completion Model Based on Hidden Graph Structure and Relational Semantic Enhancement

Knowledge Graph Completion (KGC) endeavors to use existing knowledge graph data for predicting missing elements in triples. Recently, due to the efficiency of graph neural networks (GNNs) in capturing topological structures and the effectiveness of text descriptions in supplementing semantic information, numerous models integrating graph structures and entity descriptions have emerged. However, these approaches typically focus on aggregating neighboring feature information and overlook mining hidden structural information. Furthermore, they often append textual descriptions to entities independently without considering the semantics within specific relations. Hence, we propose the SRE-KGC model to address these challenges. First, while aggregating neighbors, we analyze and mine the hidden structure in the neighborhood from the perspective of entities and relations; then we introduced a dual-layer attention mechanism to extract the most pertinent textual information towards relations from both the relational semantic level and the neighbor semantic level respectively; finally, the two learned features are fused and sent to the decoder for scoring. Experiments demonstrate that our model delivers superior performance.