Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the same real-world object. Recent advancement in KG embedding impels the advent of embedding-based entity alignment, which encodes entities in a continuous embedding space and measures entity similarities based on the learned embeddings. In this paper, we conduct a comprehensive experimental study of this emerging field. We survey 23 recent embedding-based entity alignment approaches and categorize them based on their techniques and characteristics. We also propose a new KG sampling algorithm, with which we generate a set of dedicated benchmark datasets with various heterogeneity and distributions for a realistic evaluation. We develop an open-source library including 12 representative embedding-based entity alignment approaches, and extensively evaluate these approaches, to understand their strengths and limitations. Additionally, for several directions that have not been explored in current approaches, we perform exploratory experiments and report our preliminary findings for future studies. The benchmark datasets, open-source library and experimental results are all accessible online and will be duly maintained.

中文翻译：

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基于嵌入的知识图实体对齐的基准研究

实体对齐试图在不同的知识图谱 (KG) 中找到引用同一现实世界对象的实体。KG 嵌入的最新进展推动了基于嵌入的实体对齐的出现，它在连续的嵌入空间中对实体进行编码，并根据学习到的嵌入来测量实体的相似性。在本文中，我们对这一新兴领域进行了全面的实验研究。我们调查了最近 23 种基于嵌入的实体对齐方法，并根据它们的技术和特征对它们进行分类。我们还提出了一种新的 KG 采样算法，通过该算法我们生成了一组具有各种异质性和分布的专用基准数据集，以进行实际评估。我们开发了一个开源库，包括 12 种代表性的基于嵌入的实体对齐方法，并广泛评估这些方法，以了解它们的优点和局限性。此外，对于当前方法中尚未探索的几个方向，我们进行了探索性实验并报告了我们的初步研究结果以供未来研究。基准数据集、开源库和实验结果都可以在线访问并得到适当维护。