Entity alignment (EA) aims at identifying equivalent entity pairs across
different knowledge graphs (KGs) that refer to the same real-world identity. To
systematically combat confirmation bias for pseudo-labeling-based entity
alignment, we propose a Unified Pseudo-Labeling framework for Entity Alignment
(UPL-EA) that explicitly eliminates pseudo-labeling errors to boost the
accuracy of entity alignment. UPL-EA consists of two complementary components:
(1) The Optimal Transport (OT)-based pseudo-labeling uses discrete OT modeling
as an effective means to enable more accurate determination of entity
correspondences across two KGs and to mitigate the adverse impact of erroneous
matches. A simple but highly effective criterion is further devised to derive
pseudo-labeled entity pairs that satisfy one-to-one correspondences at each
iteration. (2) The cross-iteration pseudo-label calibration operates across
multiple consecutive iterations to further improve the pseudo-labeling
precision rate by reducing the local pseudo-label selection variability with a
theoretical guarantee. The two components are respectively designed to
eliminate Type I and Type II pseudo-labeling errors identified through our
analyse. The calibrated pseudo-labels are thereafter used to augment prior
alignment seeds to reinforce subsequent model training for alignment inference.
The effectiveness of UPL-EA in eliminating pseudo-labeling errors is both
theoretically supported and experimentally validated. The experimental results
show that our approach achieves competitive performance with limited prior
alignment seeds.

本文提出了一种统一的伪标记框架，即 UPL-EA，以系统地对抗基于伪标记的实体对齐的确认偏差，该框架由基于最优输运和跨迭代伪标记校准两部分组成，分别设计用来消除识别虚假实体对齐的两种类型伪标记。实验表明，该方法能够在有限真实实体对齐种子的情况下取得竞争性的性能。