TCKGE: Transformers with contrastive learning for knowledge graph embedding

Xiaowei Zhang, Quan Fang, Jun Hu, Shengsheng Qian, Changsheng Xu

Abstract: Representation learning of knowledge graphs has emerged as a powerful technique for various downstream tasks. In recent years, numerous research efforts have been made for knowledge graphs embedding. However, previous approaches usually have difficulty dealing with complex multi-relational knowledge graphs due to their shallow network architecture. In this paper, we propose a novel framework named Transformers with Contrastive learning for Knowledge Graph Embedding (TCKGE), which aims to learn complex semantics in multi-relational knowledge graphs with deep architectures. To effectively capture the rich semantics of knowledge graphs, our framework leverages the powerful Transformers to build a deep hierarchical architecture to dynamically learn the embeddings of entities and relations. To obtain more robust knowledge embeddings with our deep architecture, we design a contrastive learning scheme to facilitate optimization by exploring the effectiveness of several different data augmentation strategies. The experimental results on two benchmark datasets show the superior of TCKGE over state-of-the-art models.