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Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org

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Listed:
  • Olivier Binette
  • Sokhna A York
  • Emma Hickerson
  • Youngsoo Baek
  • Sarvo Madhavan
  • Christina Jones

Abstract

This article introduces a novel evaluation methodology for entity resolution algorithms. It is motivated by PatentsView.org, a public-use patent data exploration platform that disambiguates patent inventors using an entity resolution algorithm. We provide a data collection methodology and tailored performance estimators that account for sampling biases. Our approach is simple, practical, and principled—key characteristics that allow us to paint the first representative picture of PatentsView’s disambiguation performance. The results are used to inform PatentsView’s users of the reliability of the data and to allow the comparison of competing disambiguation algorithms.

Suggested Citation

  • Olivier Binette & Sokhna A York & Emma Hickerson & Youngsoo Baek & Sarvo Madhavan & Christina Jones, 2023. "Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org," The American Statistician, Taylor & Francis Journals, vol. 77(4), pages 370-380, October.
    • Handle: RePEc:taf:amstat:v:77:y:2023:i:4:p:370-380
    DOI: 10.1080/00031305.2023.2191664
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