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Use of ResearchGate and Google CSE for author name disambiguation

Author

Listed:
  • Mehmet Ali Abdulhayoglu

    (KU Leuven)

  • Bart Thijs

    (KU Leuven)

Abstract

Author name disambiguation plays a very important role in individual based bibliometric analysis and has suffered from lack of information. Therefore, some have tried to leverage external web sources to obtain additional evidence with success. However, the main problem is generally the high cost of extracting data from web pages due to their diverse designs. Considering this challenge, we employed ResearchGate (RG), a social network platform for scholars presenting their publication lists in a structured way. Even though the platform might be imperfect, it can be valuable when it is used along with traditional approaches for the purpose of confirmation. To this end, in our first (retrieval) stage we applied a graph based machine learning approach, connected components (CC) and formed clusters. Then, the data crawled from RG for the same authors were combined with the CC results in stage 2. We observed that 76.40% of the clusters formed by CC were confirmed by the RG data and they accounted for 68.33% of all citations. Second, a subset was drawn from the dataset by retaining those clusters having at least 10 members to examine the details. This time we additionally employed the Google Custom Search Engine (CSE) API to access authors’ web pages as a complementary tool to RG. We observed an F score of 0.95 when CC results were confirmed by RG&CSE. Almost the same success was observed when only the CC approach was applied. In addition, we observed that the publications identified and confirmed through the external sources were cited to a greater extent than those publications not found in the related external sources. Even though promising, there are still issues with the use of external sources. We have seen that many authors present only a few selected papers on the web. This hampers our procedure, making it unable to obtain the entire publication list. Missing publications affect bibliometric analysis adversely since all citation data is required. That is, if only the data confirmed via external sources is used, bibliometric indicators will be overestimated. On the other hand, our suggested methodology can potentially decrease the manual work required for individual based bibliometric analysis. The procedure may also present more reliable results by confirming cluster members derived from unsupervised grouping methods. This approach might be especially beneficial for large datasets where extensive manual work would otherwise be required.

Suggested Citation

  • Mehmet Ali Abdulhayoglu & Bart Thijs, 2017. "Use of ResearchGate and Google CSE for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1965-1985, June.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:3:d:10.1007_s11192-017-2341-y
    DOI: 10.1007/s11192-017-2341-y
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    References listed on IDEAS

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    1. 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.
    2. Ciriaco Andrea D'Angelo & Cristiano Giuffrida & Giovanni Abramo, 2011. "A heuristic approach to author name disambiguation in bibliometrics databases for large‐scale research assessments," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 257-269, February.
    3. Ortega, José Luis, 2015. "Relationship between altmetric and bibliometric indicators across academic social sites: The case of CSIC's members," Journal of Informetrics, Elsevier, vol. 9(1), pages 39-49.
    4. Song, Min & Kim, Erin Hea-Jin & Kim, Ha Jin, 2015. "Exploring author name disambiguation on PubMed-scale," Journal of Informetrics, Elsevier, vol. 9(4), pages 924-941.
    5. Mehmet Ali Abdulhayoglu & Bart Thijs & Wouter Jeuris, 2016. "Using character n-grams to match a list of publications to references in bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1525-1546, December.
    6. Mike Thelwall & Kayvan Kousha, 2015. "ResearchGate: Disseminating, communicating, and measuring Scholarship?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(5), pages 876-889, May.
    7. Ciriaco Andrea D'Angelo & Cristiano Giuffrida & Giovanni Abramo, 2011. "A heuristic approach to author name disambiguation in bibliometrics databases for large-scale research assessments," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 257-269, February.
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    Cited by:

    1. Ciriaco Andrea D’Angelo & Nees Jan Eck, 2020. "Collecting large-scale publication data at the level of individual researchers: a practical proposal for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 883-907, May.
    2. Min Song & Keun Young Kang & Tatsawan Timakum & Xinyuan Zhang, 2020. "Examining influential factors for acknowledgements classification using supervised learning," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.
    3. Rehs, Andreas, 2021. "A supervised machine learning approach to author disambiguation in the Web of Science," Journal of Informetrics, Elsevier, vol. 15(3).
    4. Weiwei Yan & Yin Zhang & Wendy Bromfield, 2018. "Analyzing the follower–followee ratio to determine user characteristics and institutional participation differences among research universities on ResearchGate," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 299-316, April.

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