KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
This work improves knowledge graph embeddings for applications like recommendation and question answering, but it is incremental as it builds on existing tensor decomposition methods.
The paper tackles the problem of knowledge graph embedding by addressing the ignored semantic similarity between related entities and entity-relation couples in different triples, proposing a contrastive learning framework for tensor decomposition-based methods that achieves new state-of-the-art results, such as 51.2% MRR on WN18RR and 59.1% MRR on YAGO3-10.
Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering. In recent years, many research efforts have been proposed for knowledge graph embedding (KGE). However, most previous KGE methods ignore the semantic similarity between the related entities and entity-relation couples in different triples since they separately optimize each triple with the scoring function. To address this problem, we propose a simple yet efficient contrastive learning framework for tensor decomposition based (TDB) KGE, which can shorten the semantic distance of the related entities and entity-relation couples in different triples and thus improve the performance of KGE. We evaluate our proposed method on three standard KGE datasets: WN18RR, FB15k-237 and YAGO3-10. Our method can yield some new state-of-the-art results, achieving 51.2% MRR, 46.8% Hits@1 on the WN18RR dataset, 37.8% MRR, 28.6% Hits@1 on FB15k-237 dataset, and 59.1% MRR, 51.8% Hits@1 on the YAGO3-10 dataset.