Entity Resolution in Practice: Lessons from a Self-Serve Pipeline

Abstract:We built and evaluated a self-serve entity resolution (ER) system on six benchmarks spanning 864 to 5M records, and three lessons emerged that are absent from existing ER literature. (1) No single matching algorithm wins everywhere - a self-serve pipeline cannot predict its next dataset, so we recommend training several algorithm families per dataset and letting an automatic bake-off pick the winner. (2) Precision and recall need separate fixes, not a shared threshold - precision needs hard rule-based vetoes, recall needs more diverse candidate retrieval. (3) One false-positive link can silently merge unrelated entities - assuming "A matches B" and "B matches C" implies "A matches C" lets a single bad link chain hundreds of records together, so every cross-group merge must be actively re-verified. We hope these lessons save practitioners the months of dead-end experiments that led us to them.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.26298 [cs.LG]

(or arXiv:2607.26298v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.26298 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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