Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for
  Entity Alignment
Qijie Ding Jie Yin Daokun Zhang Junbin Gao
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.