The proliferation of Knowledge Graphs (KGs) that support a wide variety of applications, like entity search, question answering and recommender systems, has led to the need for identifying overlapping information among different KGs. Entity Alignment (EA) is the problem of detecting such overlapping information among KGs that refer to the same real-world entities. Recent works have shown a great potential in exploiting KG embeddings for the task of EA, with most works focusing on the structural representation of entities (i.e., entity neighborhoods) in a KG and some works also exploiting the available factual information of entities (e.g., their names and associated literal values). However, real-word KGs exhibit high levels of structural and semantic heterogeneity, making EA a challenging task in which most existing methods struggle to achieve good results. In this work, we propose HybEA, an open-source EA method that focuses on both structure and facts, using two separate attention-based models. Our experimental results show that HybEA outperforms state-of-the-art methods by at least 5% and as much as 20+% (with an average difference of 11+%) Hits@1, in 5 widely used benchmark datasets.

翻译：知识图谱（KGs）的激增支持了多种应用，如实体搜索、问答和推荐系统，这导致需要识别不同知识图谱之间的重叠信息。实体对齐（EA）旨在检测不同知识图谱中指向同一现实世界实体的重叠信息。最近的研究表明，利用知识图谱嵌入进行实体对齐任务具有巨大潜力，其中大多数工作侧重于知识图谱中实体的结构表示（即实体邻域），部分研究也利用了实体的可用事实信息（例如实体名称和关联的字面量值）。然而，现实世界中的知识图谱表现出高度的结构异质性和语义异质性，使得实体对齐成为一项具有挑战性的任务，大多数现有方法难以取得良好效果。在本工作中，我们提出了HybEA，一种开源的实体对齐方法，该方法同时关注结构和事实信息，并采用两个独立的基于注意力的模型。我们的实验结果表明，在5个广泛使用的基准数据集上，HybEA在Hits@1指标上优于现有最先进方法至少5%，最高超过20%以上（平均差异超过11%）。