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A novel multiple layers name disambiguation framework for digital libraries using dynamic clustering

Author

Listed:
  • Jia Zhu

    (South China Normal University)

  • Xingcheng Wu

    (South China Normal University)

  • Xueqin Lin

    (South China Normal University)

  • Changqin Huang

    (South China Normal University)

  • Gabriel Pui Cheong Fung

    (The Chinese University of Hong Kong)

  • Yong Tang

    (South China Normal University)

Abstract

In many types of databases, such as a science bibliography database, the name attribute is the most commonly used identifier to recognize entities. However, names are frequently ambiguous and not always unique, thereby causing problems in various fields. Name disambiguation is a data management task that aims to properly distinguish different entities that share the same name, particularly for large databases such as digital libraries, because the information that can be used to identify author’s name is limited. In digital libraries, the issue of ambiguous author names occurs due to the existence of multiple authors with the same name or different name variations for the same author. Most previous works conducted to solve this issue frequently used hierarchical clustering approaches based on information within citation records, e.g., co-authors and publication titles. In the present study, we propose a multiple layers name disambiguation framework that is not only applicable to digital libraries but can also be easily extended to other applications. Our framework adopts a dynamic clustering mechanism to minimize clustering errors. We evaluated our approach on real world corpora, and favorable experiment results indicated that our proposed framework was feasible.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:114:y:2018:i:3:d:10.1007_s11192-017-2611-8
    DOI: 10.1007/s11192-017-2611-8
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Jia Zhu & Yi Yang & Qing Xie & Liwei Wang & Saeed-Ul Hassan, 2014. "Robust hybrid name disambiguation framework for large databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 2255-2274, March.
    5. Diego R. Amancio & Osvaldo N. Oliveira jr & Luciano F. Costa, 2015. "Topological-collaborative approach for disambiguating authors’ names in collaborative networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 465-485, January.
    6. Gabor J. Szekely & Maria L. Rizzo, 2005. "Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 151-183, September.
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    Cited by:

    1. 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.
    2. Humaira Waqas & Muhammad Abdul Qadir, 2021. "Multilayer heuristics based clustering framework (MHCF) for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7637-7678, September.
    3. Humaira Waqas & Abdul Qadir, 2022. "Completing features for author name disambiguation (AND): an empirical analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 1039-1063, February.
    4. Jinseok Kim & Jenna Kim & Jason Owen‐Smith, 2021. "Ethnicity‐based name partitioning for author name disambiguation using supervised machine learning," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 979-994, August.
    5. 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.

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