Enhancing Knowledge Graph Completion With Structural-Semantic Integration and Contrastive Learning

Knowledge graph completion (KGC) addresses the issue of incomplete knowledge graphs by inferring missing triples, which is crucial for information retrieval, question answering, and recommender systems. Existing KGC methods generally focus on either exploiting the graph’s structural topology or leveraging the semantic information from entity descriptions. However, existing approaches often overlook the synergy between structure information and semantic information. To address the existing shortcomings, we proposeStrucSem, a novel model that leverages both structural and semantic information to boost KGC performance. Our model: 1) encodes the graph’s structural information by aggregating neighborhood data around the query entity using an attention mechanism; and 2) combines this with the encoding of textual descriptions, facilitating the integration of both types of information. Additionally, we extend contrastive learning to incorporate multiple positive samples, improving the model’s ability to represent diverse relational patterns. Our approach significantly enhances KGC performance, as demonstrated through extensive evaluations on standard benchmark datasets. The results highlight the superiority of combining structural and semantic information, offering new insights into improving KGC tasks.