Role Support in Knowledge-Graph Error Ranking: Predictor Regimes and Evaluation Policies

Abstract:When do role-specific training counts improve knowledge-graph error ranking? We evaluate fifteen from-scratch predictors, nine development-selected continuation stages, and six fixed ensembles across three static graphs. A positive penalty for missing training support has no stable error-ranking orientation: stronger ComplEx training reverses its pooled error AUROC from 0.689 to 0.364 on FB15k-237, and from 0.729 to 0.430 in a prospectively specified CoDEx-S check. Pair decomposition shows that reversal also survives pair-weighted within-relation evaluation, but not equal-relation averaging. The statement depends on which relations and query pairs receive weight. Learned continuous counts remain useful: they improve AURC and Brier beyond explicit ensemble disagreement in twelve original ensemble/head comparisons and satisfy all four predefined external-dataset AURC contrasts. Binary indicators add no information beyond their defining counts and have less stable fitting effects. Candidate, target, population and acceptance policies change the comparisons; fixed development thresholds do not preserve a common test answer rate. The external study was fixed before inspection of its test outcomes, whereas subsequent explanations and reused-graph controls are explicitly exploratory. These measurements support relation-aware count baselines and policy-specific support diagnostics, not a new uncertainty decomposition, causal training mechanism, calibrated factual truth, or graph-population guarantee. Saved checkpoints, ledgers and separately authored numerical implementation replays make the scoped findings inspectable.

Machine Learning (cs.LG)

arXiv:2512.22318 [cs.LG]

(or arXiv:2512.22318v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2512.22318 Focus to learn more arXiv-issued DOI via DataCite

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