Employing Latent Categories of Entities for Knowledge Graph Embeddings With Contrastive Learning

Abstract

Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for robotics to learn and reason about semantic knowledge. It is crucial for knowledge graph embedding models to infer various relation patterns, such as symmetry/antisymmetry. However, most existing approaches ignore the latent semantic categories information of entities. For example, when robots use a faucet with relation <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OperatesOn</i> , its tail entity category should be washable objects, such as apple, bowl, rather than non-washable objects, such as mobile phone, computer. To address such issues, we propose a novel and simple framework for knowledge graph embedding, which utilizes contrastive learning to cluster entities based on the constraints arising from relations, and enhances the discriminative ability for entities with the latent same category. Experimental results demonstrate the effectiveness of our proposed method on four standard knowledge graph completion benchmarks. It is noteworthy that our method can yield some new state-of-the-art results, achieving 88.2% MRR, 84.9% Hits@1 on the AI2Thor dataset, and 39.8% MRR, 30.0% Hits@1 on the FB15k-237 dataset.