An Experimental Study of State-of-the-Art Entity Alignment Approaches

Xiang Zhao, Weixin Zeng, Jiuyang Tang, Wei Wang, Fabian Suchanek
2020 IEEE Transactions on Knowledge and Data Engineering  
Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs (KGs), which is an essential step to enhance the quality of KGs, and hence of significance to downstream applications (e.g., question answering and recommendation). Recent years have witnessed a rapid increase of EA approaches, yet the relative performance of them remains unclear, partly due to the incomplete empirical evaluations, as well as the fact that comparisons were carried out under different
more » ... ettings (i.e., datasets, information used as input, etc.). In this paper, we fill in the gap by conducting a comprehensive evaluation and detailed analysis of state-of-the-art EA approaches. We first propose a general EA framework that encompasses all the current methods, and then group existing methods into three major categories. Next, we judiciously evaluate these solutions on a wide range of use cases, based on their effectiveness, efficiency and robustness. Finally, we construct a new EA dataset to mirror the real-life challenges of alignment, which were largely overlooked by existing literature. This study strives to provide a clear picture of the strengths and weaknesses of current EA approaches, so as to inspire quality follow-up research.
doi:10.1109/tkde.2020.3018741 fatcat:c3fs64qzijcqrmormwwr6r7t2i