A knowledge graph completion model based on contrastive learning and relation enhancement method

(GATs) and contrastive learning (CL), called the CLGAT-KGC model. This model introduces the graph attention mechanism and adds different representations of entities under the same entity corresponding to different relations to enhance the entity-relation message function. Additionally, a new CL method is proposed under the CLGAT-KGC model to better learn the embedding of entities and relations in the KG domain. We have completely verified the effectiveness of this model through extensive experiments.