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Author disambiguation using multi-aspect similarity indicators

Author

Listed:
  • Thomas Gurney

    (Rathenau Institute)

  • Edwin Horlings

    (Rathenau Institute)

  • Peter van den Besselaar

    (VU University Amsterdam)

Abstract

Key to accurate bibliometric analyses is the ability to correctly link individuals to their corpus of work, with an optimal balance between precision and recall. We have developed an algorithm that does this disambiguation task with a very high recall and precision. The method addresses the issues of discarded records due to null data fields and their resultant effect on recall, precision and F-measure results. We have implemented a dynamic approach to similarity calculations based on all available data fields. We have also included differences in author contribution and age difference between publications, both of which have meaningful effects on overall similarity measurements, resulting in significantly higher recall and precision of returned records. The results are presented from a test dataset of heterogeneous catalysis publications. Results demonstrate significantly high average F-measure scores and substantial improvements on previous and stand-alone techniques.

Suggested Citation

  • Thomas Gurney & Edwin Horlings & Peter van den Besselaar, 2012. "Author disambiguation using multi-aspect similarity indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 435-449, May.
    • Handle: RePEc:spr:scient:v:91:y:2012:i:2:d:10.1007_s11192-011-0589-1
    DOI: 10.1007/s11192-011-0589-1
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    Citations

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    2. Wu, Jiang, 2013. "Investigating the universal distributions of normalized indicators and developing field-independent index," Journal of Informetrics, Elsevier, vol. 7(1), pages 63-71.
    3. 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.
    4. Jiang Wu & Xiu-Hao Ding, 2013. "Author name disambiguation in scientific collaboration and mobility cases," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 683-697, September.
    5. Trapido, Denis, 2015. "How novelty in knowledge earns recognition: The role of consistent identities," Research Policy, Elsevier, vol. 44(8), pages 1488-1500.
    6. Fernanda Morillo & Ignacio Santabárbara & Javier Aparicio, 2013. "The automatic normalisation challenge: detailed addresses identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 953-966, June.
    7. Rehs, Andreas, 2021. "A supervised machine learning approach to author disambiguation in the Web of Science," Journal of Informetrics, Elsevier, vol. 15(3).
    8. Koski, Timo & Sandström, Erik & Sandström, Ulf, 2016. "Towards field-adjusted production: Estimating research productivity from a zero-truncated distribution," Journal of Informetrics, Elsevier, vol. 10(4), pages 1143-1152.
    9. Omar Hernando Avila-Poveda, 2014. "Technical report: the trend of author compound names and its implications for authorship identity identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 833-846, October.
    10. Gurney, Thomas & Horlings, Edwin & van den Besselaar, Peter & Sumikura, Koichi & Schoen, Antoine & Laurens, Patricia & Pardo, Daniel, 2014. "Analysing knowledge capture mechanisms: Methods and a stylised bioventure case," Journal of Informetrics, Elsevier, vol. 8(1), pages 259-272.
    11. Matthijs den Besten & Catalina Martínez & Nicolas Besson & Stéphane Maraut & Jean-Michel Dalle, 2014. "Human computing via online labor markets. The perils and promises of crowdsourcing in data-rich ecosystems," Working Papers 1402, Instituto de Políticas y Bienes Públicos (IPP), CSIC.

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