Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment

• URL: http://arxiv.org/abs/2307.02075v4
• Date: Wed, 02 Jul 2025 01:04:31 GMT
• Title: Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment
• Authors: Qijie Ding, Jie Yin, Daokun Zhang, Junbin Gao,
• Abstract summary: We propose a Unified Pseudo-Labeling framework for Entity Alignment (UPL-EA)<n>UPL-EA explicitly eliminates pseudo-labeling errors to boost the accuracy of entity alignment.<n>Our results and in-depth analyses demonstrate the superiority of UPL-EA over 15 competitive baselines.
• Score: 30.407534668054286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity alignment (EA) aims at identifying equivalent entity pairs across different knowledge graphs (KGs) that refer to the same real-world identity. To circumvent the shortage of seed alignments provided for training, recent EA models utilize pseudo-labeling strategies to iteratively add unaligned entity pairs predicted with high confidence to the seed alignments for model training. However, the adverse impact of confirmation bias during pseudo-labeling has been largely overlooked, thus hindering entity alignment performance. 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) Optimal Transport (OT)-based pseudo-labeling uses discrete OT modeling as an effective means to determine entity correspondences and reduce erroneous matches across two KGs. An effective criterion is derived to infer pseudo-labeled alignments that satisfy one-to-one correspondences; (2) Parallel pseudo-label ensembling refines pseudo-labeled alignments by combining predictions over multiple models independently trained in parallel. The ensembled pseudo-labeled alignments are thereafter used to augment seed alignments 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. Our extensive results and in-depth analyses demonstrate the superiority of UPL-EA over 15 competitive baselines and its utility as a general pseudo-labeling framework for entity alignment.

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