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

• URL: http://arxiv.org/abs/2210.01230v2
• Date: Mon, 17 Apr 2023 21:38:23 GMT
• Title: Estimating the Performance of Entity Resolution Algorithms: Lessons Learned Through PatentsView.org
• Authors: Olivier Binette, Sokhna A York, Emma Hickerson, Youngsoo Baek, Sarvo Madhavan, Christina Jones
• Abstract summary: 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.
• Score: 3.8494315501944736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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