  Embedding-based entity alignment has been widely investigated in recent
years, but most proposed methods still rely on an ideal supervised learning
setting with a large number of unbiased seed mappings for training and
validation, which significantly limits their usage. In this study, we evaluate
those state-of-the-art methods in an industrial context, where the impact of
seed mappings with different sizes and different biases is explored. Besides
the popular benchmarks from DBpedia and Wikidata, we contribute and evaluate a
new industrial benchmark that is extracted from two heterogeneous knowledge
graphs (KGs) under deployment for medical applications. The experimental
results enable the analysis of the advantages and disadvantages of these
alignment methods and the further discussion of suitable strategies for their
industrial deployment.
