Knowledge graph embedding (KGE) aims to learn powerful representations to
benefit various artificial intelligence applications, such as question
answering and recommendations. Meanwhile, contrastive learning (CL), as an
effective mechanism to enhance the discriminative capacity of the learned
representations, has been leveraged in different fields, especially graph-based
models. However, since the structures of knowledge graphs (KGs) are usually
more complicated compared to homogeneous graphs, it is hard to construct
appropriate contrastive sample pairs. In this paper, we find that the entities
within a symmetrical structure are usually more similar and correlated. This
key property can be utilized to construct contrastive positive pairs for
contrastive learning. Following the ideas above, we propose a relational
symmetrical structure based knowledge graph contrastive learning framework,
termed KGE-SymCL, which leverages the symmetrical structure information in KGs
to enhance the discriminative ability of KGE models. Concretely, a
plug-and-play approach is designed by taking the entities in the relational
symmetrical positions as the positive samples. Besides, a self-supervised
alignment loss is used to pull together the constructed positive sample pairs
for contrastive learning. Extensive experimental results on benchmark datasets
have verified the good generalization and superiority of the proposed
framework.

本文介绍了一种基于关系对称结构的知识图谱对比学习框架 KGE-SymCL，通过设计可插拔的方法将关系对称位置中的实体作为正样本用于对比学习，同时使用自监督对齐损失来使构建的样本对更加相似，实验结果表明该框架具有很好的泛化能力和优越性。