Evaluates detection and recalibration methods for calibration drift in knowledge graphs, suggesting improvements.
Paper 13 in the Prioris research series. Addresses calibration drift in continuously updated knowledge graphs. Evaluates CUSUM, EWMA, and KS-test detection methods across four drift scenarios. Compares Platt re-fit, isotonic regression, and Bayesian update recalibration strategies. 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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