What does this research mean for the field?

Confidence calibration methods can achieve expected calibration error (ECE) < 0.05 for inference-level edges in knowledge graphs with at least 500 samples. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to improve the accuracy of knowledge graph edge calibration using various scaling methods and metrics.

March 16, 2026Open Access

Confidence Calibration for Evidence-Graded Knowledge Graph Edges: Methods, Metrics, and Cold-Start Protocols

Q: What question did this study set out to answer?

This research aims to improve the accuracy of knowledge graph edge calibration using various scaling methods and metrics.

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Key Points

This research aims to improve the accuracy of knowledge graph edge calibration using various scaling methods and metrics.
Analyzed Platt scaling, isotonic regression, and temperature scaling for calibration.
Proposed level-specific quality metrics to assess edge calibration.
Developed a cold-start calibration protocol for initial predictions.
Achievable expected calibration error (ECE) of less than 0.05 for inference-level edges.
Requires a minimum of 500 samples for reliable calibration outcomes.

Abstract

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 = 500 samples. Part of the Evidence-Graded Knowledge Graphs research programme (Paper 0: 10.5281/zenodo.19024611).

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Rogier Meulenaar

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Radboud University Nijmegen

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Confidence Calibration for Evidence-Graded Knowledge Graph Edges: Methods, Metrics, and Cold-Start Protocols

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