Entity Alignment (EA) identifies entities across databases that refer to the
same entity. Knowledge graph-based embedding methods have recently dominated EA
techniques. Such methods map entities to a low-dimension space and align them
based on their similarities. With the corpus of EA methodologies growing
rapidly, this paper presents a comprehensive analysis of various existing EA
methods, elaborating their applications and limitations. Further, we
distinguish the methods based on their underlying algorithms and the
information they incorporate to learn entity representations. Based on
challenges in industrial datasets, we bring forward $4$ research questions
(RQs). These RQs empirically analyse the algorithms from the perspective of
\textit{Hubness, Degree distribution, Non-isomorphic neighbourhood,} and
\textit{Name bias}. For Hubness, where one entity turns up as the nearest
neighbour of many other entities, we define an $h$-score to quantify its effect
on the performance of various algorithms. Additionally, we try to level the
playing field for algorithms that rely primarily on name-bias existing in the
benchmarking open-source datasets by creating a low name bias dataset. We
further create an open-source repository for $14$ embedding-based EA methods
and present the analysis for invoking further research motivations in the field
of EA.

本文对不同的实体对齐方法进行了全面分析，其中主要以基于知识图谱的低维嵌入算法为主，同时提出了四个研究问题并对应用场景和局限性进行了讨论，根据工业数据集中的难点以	extit {Hubness, Degree distribution, Non-isomorphic neighbourhood,} 和	extit {Name bias} 等四个方面进行了实证分析，提出了一个低名称偏倚数据集，并创建了一个开源库，包括 14 种嵌入式实体对齐方法用于进一步研究。