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A fast and integrative algorithm for clustering performance evaluation in author name disambiguation

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  • Jinseok Kim

    (University of Michigan)

Abstract

Clustering results in author name disambiguation are often evaluated by measures such as Cluster-F, K-metric, Pairwise-F, Splitting and Lumping Error, and B-cubed. Although these measures have different evaluation approaches, this paper shows that they can be calculated in a single framework by a set of common steps that compare truth and predicted clusters through two hash tables recording information about name instances with their predicted cluster indices and frequencies of those indices per truth cluster. This integrative calculation reduces greatly calculation runtime, which is scalable to a clustering task involving millions of name instances within a few seconds. During the integration process, B-cubed and K-metric are shown to produce the same precision and recall scores. In addition, name instance pairs for Pairwise-F are counted using a heuristic, which enables the proposed method to surpass a state-of-the-art algorithm in speedy calculation. Details of the integrative calculation are described with examples and pseudo-code to assist scholars to implement each measure easily and validate the correctness of implementation. The integrative calculation will help scholars compare similarities and differences of multiple measures before they select ones that characterize best the clustering performances of their disambiguation methods.

Suggested Citation

  • Jinseok Kim, 2019. "A fast and integrative algorithm for clustering performance evaluation in author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 661-681, August.
    • Handle: RePEc:spr:scient:v:120:y:2019:i:2:d:10.1007_s11192-019-03143-7
    DOI: 10.1007/s11192-019-03143-7
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    References listed on IDEAS

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    1. Jinseok Kim & Jenna Kim, 2018. "The impact of imbalanced training data on machine learning for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 511-526, October.
    2. Kim, Jinseok & Diesner, Jana, 2015. "The effect of data pre-processing on understanding the evolution of collaboration networks," Journal of Informetrics, Elsevier, vol. 9(1), pages 226-236.
    3. Yu Liu & Weijia Li & Zhen Huang & Qiang Fang, 2015. "A fast method based on multiple clustering for name disambiguation in bibliographic citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(3), pages 634-644, March.
    4. Ricardo G. Cota & Anderson A. Ferreira & Cristiano Nascimento & Marcos André Gonçalves & Alberto H. F. Laender, 2010. "An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(9), pages 1853-1870, September.
    5. Wanli Liu & Rezarta Islamaj Doğan & Sun Kim & Donald C. Comeau & Won Kim & Lana Yeganova & Zhiyong Lu & W. John Wilbur, 2014. "Author name disambiguation for PubMed," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 765-781, April.
    6. Brent D Fegley & Vetle I Torvik, 2013. "Has Large-Scale Named-Entity Network Analysis Been Resting on a Flawed Assumption?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-16, July.
    7. Dongwook Shin & Taehwan Kim & Joongmin Choi & Jungsun Kim, 2014. "Author name disambiguation using a graph model with node splitting and merging based on bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 15-50, July.
    8. Lutz Bornmann & Rüdiger Mutz, 2015. "Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2215-2222, November.
    9. Ricardo G. Cota & Anderson A. Ferreira & Cristiano Nascimento & Marcos André Gonçalves & Alberto H. F. Laender, 2010. "An unsupervised heuristic‐based hierarchical method for name disambiguation in bibliographic citations," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(9), pages 1853-1870, September.
    10. Hongqi Han & Changqing Yao & Yuan Fu & Yongsheng Yu & Yunliang Zhang & Shuo Xu, 2017. "Semantic fingerprints-based author name disambiguation in Chinese documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1879-1896, June.
    11. Hao Wu & Bo Li & Yijian Pei & Jun He, 2014. "Unsupervised author disambiguation using Dempster–Shafer theory," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1955-1972, December.
    12. Michael Levin & Stefan Krawczyk & Steven Bethard & Dan Jurafsky, 2012. "Citation‐based bootstrapping for large‐scale author disambiguation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(5), pages 1030-1047, May.
    13. Mark-Christoph Müller & Florian Reitz & Nicolas Roy, 2017. "Data sets for author name disambiguation: an empirical analysis and a new resource," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1467-1500, June.
    14. Andreas Strotmann & Dangzhi Zhao, 2012. "Author name disambiguation: What difference does it make in author-based citation analysis?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(9), pages 1820-1833, September.
    15. Jia Zhu & Xingcheng Wu & Xueqin Lin & Changqin Huang & Gabriel Pui Cheong Fung & Yong Tang, 2018. "A novel multiple layers name disambiguation framework for digital libraries using dynamic clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 781-794, March.
    16. Andreas Strotmann & Dangzhi Zhao, 2012. "Author name disambiguation: What difference does it make in author‐based citation analysis?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(9), pages 1820-1833, September.
    17. Anderson A. Ferreira & Adriano Veloso & Marcos André Gonçalves & Alberto H. F. Laender, 2014. "Self-training author name disambiguation for information scarce scenarios," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(6), pages 1257-1278, June.
    18. Alan Filipe Santana & Marcos André Gonçalves & Alberto H. F. Laender & Anderson A. Ferreira, 2017. "Incremental author name disambiguation by exploiting domain-specific heuristics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 931-945, April.
    19. Agustín D. Delgado & Raquel Martínez & Soto Montalvo & Víctor Fresno, 2017. "Person Name Disambiguation in the Web Using Adaptive Threshold Clustering," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(7), pages 1751-1762, July.
    20. Jinseok Kim, 2018. "Evaluating author name disambiguation for digital libraries: a case of DBLP," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1867-1886, September.
    21. Jinseok Kim & Jana Diesner, 2016. "Distortive effects of initial-based name disambiguation on measurements of large-scale coauthorship networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(6), pages 1446-1461, June.
    22. Michael Levin & Stefan Krawczyk & Steven Bethard & Dan Jurafsky, 2012. "Citation-based bootstrapping for large-scale author disambiguation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(5), pages 1030-1047, May.
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    Cited by:

    1. Rehs, Andreas, 2021. "A supervised machine learning approach to author disambiguation in the Web of Science," Journal of Informetrics, Elsevier, vol. 15(3).
    2. Jinseok Kim & Jason Owen-Smith, 2021. "ORCID-linked labeled data for evaluating author name disambiguation at scale," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2057-2083, March.
    3. Jinseok Kim & Jenna Kim, 2020. "Effect of forename string on author name disambiguation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(7), pages 839-855, July.
    4. KM. Pooja & Samrat Mondal & Joydeep Chandra, 2021. "Exploiting similarities across multiple dimensions for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7525-7560, September.

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