  In recent years, we have witnessed the proliferation of knowledge graphs (KG)
in various domains, aiming to support applications like question answering,
recommendations, etc. A frequent task when integrating knowledge from different
KGs is to find which subgraphs refer to the same real-world entity. Recently,
embedding methods have been used for entity alignment tasks, that learn a
vector-space representation of entities which preserves their similarity in the
original KGs. A wide variety of supervised, unsupervised, and semi-supervised
methods have been proposed that exploit both factual (attribute based) and
structural information (relation based) of entities in the KGs. Still, a
quantitative assessment of their strengths and weaknesses in real-world KGs
according to different performance metrics and KG characteristics is missing
from the literature. In this work, we conduct the first meta-level analysis of
popular embedding methods for entity alignment, based on a statistically sound
methodology. Our analysis reveals statistically significant correlations of
different embedding methods with various meta-features extracted by KGs and
rank them in a statistically significant way according to their effectiveness
across all real-world KGs of our testbed. Finally, we study interesting
trade-offs in terms of methods' effectiveness and efficiency.
