  Embedding-based entity alignment (EEA) has recently received great attention.
Despite significant performance improvement, few efforts have been paid to
facilitate understanding of EEA methods. Most existing studies rest on the
assumption that a small number of pre-aligned entities can serve as anchors
connecting the embedding spaces of two KGs. Nevertheless, no one investigates
the rationality of such an assumption. To fill the research gap, we define a
typical paradigm abstracted from existing EEA methods and analyze how the
embedding discrepancy between two potentially aligned entities is implicitly
bounded by a predefined margin in the scoring function. Further, we find that
such a bound cannot guarantee to be tight enough for alignment learning. We
mitigate this problem by proposing a new approach, named NeoEA, to explicitly
learn KG-invariant and principled entity embeddings. In this sense, an EEA
model not only pursues the closeness of aligned entities based on geometric
distance, but also aligns the neural ontologies of two KGs by eliminating the
discrepancy in embedding distribution and underlying ontology knowledge. Our
experiments demonstrate consistent and significant improvement in performance
against the best-performing EEA methods.
