OpenEA dataset v2.0

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. This study surveys 23 recent embedding-based entity alignment approaches and categorizes them based on their techniques and characteristics. We further observe that current approaches use different datasets in evaluation, and the degree distributions of entities in these datasets are inconsistent with real KGs. Hence, we 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. This study also produces an open-source library, which includes 12 representative embedding-based entity alignment approaches. We extensively evaluate these approaches on the generated datasets, 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.实体对齐（Entity Alignment）旨在查找不同知识图谱（Knowledge Graphs，KGs）中指向同一真实世界对象的实体。近年来，知识图谱嵌入（KG Embedding）技术的发展推动了基于嵌入的实体对齐方法的兴起，这类方法将实体编码至连续嵌入空间，并基于学习得到的嵌入向量度量实体间的相似度。本文针对这一新兴领域开展了全面的实验研究：本研究调研了23种近年提出的基于嵌入的实体对齐方法，并依据其技术路线与特性进行了分类。我们进一步发现，现有方法在评估阶段采用的数据集各不相同，且这些数据集中实体的度分布与真实知识图谱的度分布并不一致。为此，我们提出了一种全新的知识图谱采样算法，并借此生成了一系列具备多样异质性与分布特征的专用基准数据集，用于开展贴合实际场景的评估。本研究同时开源了一个工具库，其中涵盖了12种具有代表性的基于嵌入的实体对齐方法。我们在生成的数据集上对这些方法进行了全面评估，以明晰它们的优势与局限性。此外，针对当前方法尚未探索的若干研究方向，我们开展了探索性实验并报告了初步发现，以供后续研究参考。本研究生成的基准数据集、开源工具库以及实验结果均已在线公开，并将得到妥善维护。