  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.
