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

The rapid development in knowledge graph (KG) technology and its popularity in the field of artificial intelligence (AI) have significantly increased the support for similar KG-based applications. However, there is a concerning problem regarding KGs; most of them are often incomplete. This motivated us to study knowledge graph completion (KGC). Some recent studies have used graph neural networks (GNN) such as graph convolutional networks (GCN) to model graph-structured data, providing good results on KGC tasks. However, the edge weights in GCN models are controlled by degree, a measure that moderately ignores the differences among relation information. To address the above limitations and obtain better KGC, we propose a model based on graph attention networks (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.