Relation-Aware Multi-Positive Contrastive Knowledge Graph Completion with Embedding Dimension Scaling

Recently, a large amount of work has emerged for knowledge graph completion (KGC), which aims to reason over known facts and to infer the missing links. Meanwhile, contrastive learning has been applied to the KGC tasks, which can improve the representation quality of entities and relations. However, existing KGC approaches tend to improve their performance with high-dimensional embeddings and complex models, which make them suffer from large storage space and high training costs. Furthermore, contrastive loss with single positive sample learns little structural and semantic information in knowledge graphs due to the complex relation types. To address these challenges, we propose a novel knowledge graph completion model named ConKGC with the embedding dimension scaling and a relation-aware multi-positive contrastive loss. In order to achieve both space consumption reduction and model performance improvement, a new scoring function is proposed to map the raw low-dimensional embeddings of entities and relations to high-dimensional embedding space, and predict low-dimensional tail entities with latent semantic information of high-dimensional embeddings. In addition, ConKGC designs a multiple weak positive samples based contrastive loss under different relation types to maintain two important training targets, Alignment and Uniformity. This loss function and few parameters of the model ensure that ConKGC performs best and has fast convergence speed. Extensive experiments on three standard datasets confirm the effectiveness of our innovations, and the performance of ConKGC is significantly improved compared to the state-of-the-art methods.