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Large-scale name disambiguation of Chinese patent inventors (1985–2016)

  • Deyun Yin
  • Kazuyuki Motohashi
  • Jianwei DangEmail author
Article

Abstract

This study presents the first systematic disambiguation result of Chinese patent inventors in State Intellectual Property Office of China patent database from 1985 to 2016. With a list of 66,248 inventors owning rare names and a hand-labeled data of 1465 inventors, our supervised learning algorithm identified 3.99 million unique inventors from 1.84 million Chinese names referring to 14.68 million patent-inventor records. We developed a method for constructing high-quality training data from a third-party rare name list and provided evidence for its reliability when large-scale and representative hand-labeled data is crucial but expensive to obtain. To optimize clustering results on large-scale dataset with highly unbalanced distribution, we also modified robust single linkage by adding constraints to the maximum distance within clusters generated. Varying across different training and testing data, as well as clustering parameters, our algorithm could yield F1 scores to 93.36% before clustering and 99.10% after clustering, with final splitting errors of 1.05–1.34% and lumping errors of 0.21–0.83%. Besides, we also applied this framework in standardizing applicants’ names according to their text similarity and geographical information based on the high-resolution geocoding data of all addresses within mainland China.

Keywords

Disambiguation Patent Inventor Machine learning Gradient boosting decision tree Single linkage 

Notes

Acknowledgements

This work is mainly supported by the Research Institute of Economy, Trade and Industry’s (RIETI) under the project of Empirical Analysis of Innovation Ecosystems in Advancement of the Internet of Things (IoT), National Natural Science Foundation of China (NSFC, Nos. 71704025; 71503123), Scientific Cooperation Program between NSFC and Japan Society for the Promotion of Science (No. 71711540044). We also appreciate the editors’ diligent work as well as insightful and inspiring comments from two anonymous reviewers, Dr. Kenta Ikeuchi, and Mr. Zhao An.

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  1. 1.Department of Technology Management for Innovation, School of EngineeringThe University of TokyoTokyoJapan
  2. 2.Shanghai International College of Intellectual PropertyTongji UniversityShanghaiChina

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