Analysis demonstrates effective calibration methods for knowledge graph edges, implying improved accuracy in applications.
Paper 3 in the Prioris research series. Analyzes Platt scaling, isotonic regression, and temperature scaling for knowledge graph edge calibration. Proposes level-specific quality metrics and a cold-start calibration protocol. Shows ECE < 0.05 is achievable for inference-level edges with n >= 500 samples. Part of the Evidence-Graded Knowledge Graphs research programme (Paper 0: 10.5281/zenodo.19024611).
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Rogier Meulenaar (2026) studied this question.
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