Entity alignment is the task of identifying entities from different knowledge graphs (KGs) that point to the same item and is important for KG fusion. In the real world, due to the heterogeneity between different KGs, equivalent entities often have different relations around them, so it is difficult for Graph Convolutional Network (GCN) to accurately learn the relation information in the KGs. Moreover, to solve the problem regarding inadequate utilisation of relation information in entity alignment, a novel GCN-based model, joint Unsupervised Relation Alignment for Entity Alignment (URAEA), is proposed. The model first employs a novel method for calculating relation embeddings by using entity embeddings, then constructs unsupervised seed relation alignments through these relation embeddings, and finally performs entity alignment together with relation alignment. In addition, the seed entity alignments are expanded based on the generated seed relation alignments. Experiments conducted on three real-world datasets show that this approach outperforms state-of-the-art methods.

中文翻译：

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具有融合关系表示的实体对齐

实体对齐是识别来自不同知识图谱（KG）中指向同一项目的实体的任务，对于知识图谱融合非常重要。在现实世界中，由于不同知识图谱之间的异构性，等价实体周围往往具有不同的关系，因此图卷积网络（GCN）很难准确地学习知识图谱中的关系信息。此外，为了解决实体对齐中关系信息利用不足的问题，提出了一种基于GCN的新模型——实体对齐的联合无监督关系对齐（URAEA）。该模型首先采用一种新颖的方法，利用实体嵌入来计算关系嵌入，然后通过这些关系嵌入构建无监督的种子关系对齐，最后将实体对齐与关系对齐一起执行。此外，种子实体对齐基于生成的种子关系对齐进行扩展。在三个真实数据集上进行的实验表明，这种方法优于最先进的方法。