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

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.

Hierarchical Federated Learning Incentivization for Gas Usage Estimation

Accurately estimating gas usage is essential for the efficient functioning of gas distribution networks and saving operational costs. Traditional methods rely on centralized data processing, which poses privacy risks. Federated learning (FL) offers a solution to this problem by enabling local data processing on each participant, such as gas companies and heating stations. However, local training and communication overhead may discourage gas companies and heating stations from actively participating in the FL training process. To address this challenge, we propose a Hierarchical FL Incentive Mechanism for Gas Usage Estimation (HI-GAS), which has been testbedded in the ENN Group, one of the leading players in the natural gas and green energy industry. It is designed to support horizontal FL among gas companies, and vertical FL among each gas company and heating station within a hierarchical FL ecosystem, rewarding participants based on their contributions to FL. In addition, a hierarchical FL model aggregation approach is also proposed to improve the gas usage estimation performance by aggregating models at different levels of the hierarchy. The incentive scheme employs a multi-dimensional contribution-aware reward distribution function that combines the evaluation of data quality and model contribution to incentivize both gas companies and heating stations within their jurisdiction while maintaining fairness. Results of extensive experiments validate the effectiveness of the proposed mechanism.

Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment

Entity alignment aims to discover unique equivalent entity pairs with the same meaning across different knowledge graphs (KG). It has been a compelling but challenging task for knowledge integration or fusion. Existing models have primarily focused on projecting KGs into a latent embedding space to capture inherent semantics between entities for entity alignment. However, the adverse impacts of alignment conflicts have been largely overlooked during training, thus 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 of CPL-OT is to iteratively pseudo-label alignment pairs empowered with conflict-aware Optimal Transport 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 (OT) as an effective means to warrant one-to-one entity alignment between two KGs with the minimal overall transport cost. The transport cost is calculated as the rectified distance between entity embeddings obtained via graph convolution augmented with global-level semantics. Extensive experiments on benchmark datasets show that CPL-OT can markedly outperform state-of-the-art baselines under both settings with and without prior alignment seeds.