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Found 3 papers, 1 papers with code
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.
Hierarchical Federated Learning Incentivization for Gas Usage Estimation
Federated learning (FL) offers a solution to this problem by enabling local data processing on each participant, such as gas companies and heating stations.
Conflict-Aware Pseudo Labeling via Optimal Transport 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.
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