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.

翻译：知识嵌入式图(KGE)旨在学习强大的代表性,以有利于各种人工智能应用,如问答和建议等。与此同时,不同领域,特别是基于图形的模式,都利用了不同领域的对比学习(CL),作为提高学习显示的区别性能力的有效机制;然而,由于知识图形结构(KGs)与同质图形相比通常更为复杂,因此很难建立适当的对比样本配对。在本文件中,我们发现在对称结构中的实体通常比较相似和相互关联。这一关键属性可用于为对比性学习构建对比性正对正对对等的对应体。根据上述想法,我们提出基于对称结构结构结构知识图对比性学习框架,称为KGE-SymCL,利用KGs的对称结构信息提高KGE模型的偏向性能力。具体地说,通过将关系对称对称位置的实体作为正的样本来设计一个插件式方法。此外,自我校正校准的调整损失被用于将已建好的正对称抽样组合合并起来,用于对比性强度的总体数据基准学习。