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

Olivier Binette, Sokhna A York, Emma Hickerson, Youngsoo Baek, Sarvo Madhavan +1 more
10/3/2022
cs.DLcs.DBcs.LG

Official code: PatentsView/PatentsView-Evaluation (16 stars, GPL-3.0, last commit 2024-01-18).

Abstract

This paper introduces a novel evaluation methodology for entity resolution algorithms. It is motivated by PatentsView.org, a U.S. Patents and Trademarks Office patent data exploration tool 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. This approach is used to inform PatentsView's users of the reliability of the data and to allow the comparison of competing disambiguation algorithms.

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Code Implementations(1)

PatentsView/PatentsView-EvaluationOfficial100%
GPL-3.0

Evaluation and benchmarking of PatentsView disambiguation algorithms

169HTML, PythonMay 10, 20222 years agoGPL-3.0
deduplicationdisambiguationentity-resolutionpatentsviewrecord-linkage+2 more

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Cite this paper

@article{binette2022estimating,
  title  = {Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org},
  author = {Olivier Binette and Sokhna A York and Emma Hickerson and Youngsoo Baek and Sarvo Madhavan and Christina Jones},
  year   = {2022},
  eprint = {2210.01230},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2210.01230}
}

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