Abstract
Cross-lingual entity alignment identifies entity
pairs that share the same meanings but locate in
different language knowledge graphs (KGs). The
study in this paper is to address two limitations that
widely exist in current solutions: 1) the alignment
loss functions defined at the entity level serve well
the purpose of aligning labeled entities but fail to
match the whole picture of labeled and unlabeled
entities in different KGs; 2) the translation from
one domain to the other has been considered (e.g.,
X to Y by M1 or Y to X by M2). However, the
important duality of alignment between different
KGs (X to Y by M1 and Y to X by M2) is ignored. We propose a novel entity alignment framework (OTEA), which dually optimizes the entitylevel loss and group-level loss via optimal transport
theory. We also impose a regularizer on the dual
translation matrices to mitigate the effect of noise
during transformation. Extensive experimental results show that our model consistently outperforms
the state-of-the-arts with significant improvements
on alignment accuracy