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Robust hybrid name disambiguation framework for large databases

Author

Listed:
  • Jia Zhu

    (South China Normal University)

  • Yi Yang

    (Carnegie Mellon University)

  • Qing Xie

    (King Abdullah University of Science and Technology)

  • Liwei Wang

    (Wuhan University)

  • Saeed-Ul Hassan

    (COMSATS Institute of Information Technology)

Abstract

In many databases, science bibliography database for example, name attribute is the most commonly chosen identifier to identify entities. However, names are often ambiguous and not always unique which cause problems in many fields. Name disambiguation is a non-trivial task in data management that aims to properly distinguish different entities which share the same name, particularly for large databases like digital libraries, as only limited information can be used to identify authors’ name. In digital libraries, ambiguous author names occur due to the existence of multiple authors with the same name or different name variations for the same person. Also known as name disambiguation, most of the previous works to solve this issue often employ hierarchical clustering approaches based on information inside the citation records, e.g. co-authors and publication titles. In this paper, we focus on proposing a robust hybrid name disambiguation framework that is not only applicable for digital libraries but also can be easily extended to other application based on different data sources. We propose a web pages genre identification component to identify the genre of a web page, e.g. whether the page is a personal homepage. In addition, we propose a re-clustering model based on multidimensional scaling that can further improve the performance of name disambiguation. We evaluated our approach on known corpora, and the favorable experiment results indicated that our proposed framework is feasible.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:98:y:2014:i:3:d:10.1007_s11192-013-1151-0
    DOI: 10.1007/s11192-013-1151-0
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Alexander Karlsson & Björn Hammarfelt & H. Joe Steinhauer & Göran Falkman & Nasrine Olson & Gustaf Nelhans & Jan Nolin, 2015. "Modeling uncertainty in bibliometrics and information retrieval: an information fusion approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2255-2274, March.
    2. 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.
    3. Jan Schulz, 2016. "Using Monte Carlo simulations to assess the impact of author name disambiguation quality on different bibliometric analyses," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1283-1298, June.
    4. Anne-Wil Harzing, 2015. "Health warning: might contain multiple personalities—the problem of homonyms in Thomson Reuters Essential Science Indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2259-2270, December.

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