Entity alignment aims to discover unique equivalent entity pairs with the
same meaning across different knowledge graphs (KGs). Existing models have
focused on projecting KGs into a latent embedding space so that inherent
semantics between entities can be captured for entity alignment. However, the
adverse impacts of alignment conflicts have been largely overlooked during
training, thereby limiting the entity alignment performance. To address this
issue, we propose a novel Conflict-aware Pseudo Labeling via Optimal Transport
model (CPL-OT) for entity alignment. The key idea is to iteratively
pseudo-label alignment pairs empowered with conflict-aware optimal transport
(OT) modeling to boost the precision of entity alignment. CPL-OT is composed of
two key components -- entity embedding learning with global-local aggregation
and iterative conflict-aware pseudo labeling -- that mutually reinforce each
other. To mitigate alignment conflicts during pseudo labeling, we propose to
use optimal transport as an effective means to warrant one-to-one entity
alignment between two KGs with the minimal overall transport cost. Extensive
experiments on benchmark datasets validate the superiority of CPL-OT over
state-of-the-art baselines under both settings with and without prior alignment
seeds.

本研究提出了一种基于最优传输建模的冲突感知伪标记（CPL-OT）模型，用于实现不同知识图谱之间的实体对齐，并证明其在基准数据集上比现有模型具有更好的表现。