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Research questionHow can uncertain knowledge graphs predict missing confidence scores without losing global structure or training stability?Most triples in an uncertain knowledge graph lack observed confidence scores, so completion depends on semi-supervised pseudo-labels. Ignoring confidence-weighted community and hub structure can reduce prediction quality and make training unstable, particularly on dense graphs.
AI
Machine Learning
Research Paper
Statistical Machine Learning
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Latest papersRecent research connected to this question, newest first.Spectral Initialization and Scheduled Graph Smoothness for Uncertain Knowledge Graph CompletionThis applies to uncertain knowledge graphs whose triples have continuous confidence scores and are trained through confidence-distribution learning. The reported evidence uses spectral initialization and a mini-batch Dirichlet-energy regularizer on two UKG datasets, improving six of eight metric–dataset pairs, matching the prior best on two, and removing a dense-graph instability spike; broader deployment evidence is not provided.research paper · Sep 2, 2026
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