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A boosted-trees method for name disambiguation

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Abstract

This paper proposes a method for classifying true papers of a set of focal scientists and false papers of homonymous authors in bibliometric research processes. It directly addresses the issue of identifying papers that are not associated (“false”) with a given author. The proposed method has four steps: name and affiliation filtering, similarity score construction, author screening, and boosted trees classification. In this methodological paper we calculate error rates for our technique. Therefore, we needed to ascertain the correct attribution of each paper. To do this we constructed a small dataset of 4,253 papers allegedly belonging to a random sample of 100 authors. We apply the boosted trees algorithm to classify papers of authors with total false rate no higher than 30% (i.e. 3,862 papers of 91 authors). A one-run experiment achieves a testing misclassification error 0.55%, testing recall 99.84%, and testing precision 99.60%. A 50-run experiment shows that the median of testing classification error is 0.78% and mean 0.75%. Among the 90 authors in the testing set (one author only appeared in the training set), the algorithm successfully reduces the false rate to zero for 86 authors and misclassifies just one or two papers for each of the remaining four authors.

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Acknowledgments

This work was supported through a U.S. National Science Foundation grant, “Women in Science and Engineering: Network Access, Participation, and Career Outcomes” (NSF Grant # REC-0529642). We thank the NSF for their support of this work, and the NETWISE team for its collective effort in the development of these data.

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Authors and Affiliations

  1. School of Public Policy, Georgia Institute of Technology, 685 Cherry Street, Atlanta, GA, 30332-0345, USA

    Jian Wang, Kaspars Berzins, Diana Hicks, Julia Melkers, Fang Xiao & Diogo Pinheiro

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  1. Jian Wang
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  2. Kaspars Berzins
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  4. Julia Melkers
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Corresponding author

Correspondence to Jian Wang.

Appendix 1: real-world motivation and implementation

Appendix 1: real-world motivation and implementation

This study was motivated by a real-world problem confronting our research on the collaboration networks of American academic scientists. It attempts to identify clean publication sets for each focal scientist and link bibliometric data to survey data, thereby creating an extensive and rich dataset of academic scientists, their networks, and related productivity. From this approach, we hope that this methodological solution can inform other similar issues faced by other researchers.

The data for this study comes from a 2006–2009 NSF-funded national study of academic scientists and engineers in Research I universities in the United States (Women in Science and Engineering: Network Access, Participation, and Career Outcomes, Grant # REC-0529642). This NSF funded project involved a two stage online survey, collection of CV data for survey respondents, including a complete history of focal scientists’ affiliation information, and collection of lifetime bibliometric data for each focal author, requiring a name and affiliation match. The object of the name disambiguation method presented in this paper was to clean 54,853 papers of 1,315 focal scientists in five disciplines (biology (BIOL), chemistry (CHEM), computer science (CS), earth and atmospheric sciences (EAS), and electrical engineering (EE).

For the development dataset of 100 authors, we firstly excluded scientists with less than five publications, because the similarity score might be unstable if the focal author has very few papers, and then we randomly sampled 100 authors from the remaining 1,255 authors.

The method presented in this paper was implemented to clean the 54,853 papers of 1,315 authors, among them 4,253 papers from 100 authors were manually checked for supervised learning. For the remaining papers, the algorithm automatically classified 44,777 papers of 1,025 authors, 156 papers of 60 authors were left for manual checking because these authors had less than five papers, and 5,667 papers of 130 authors were left for manual checking because these authors were predicted to have high false rates. Overall, the algorithm reduced the labor required for manual data cleaning by about 80% (44,777 out of 54,853 papers were automatically cleaned).

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Wang, J., Berzins, K., Hicks, D. et al. A boosted-trees method for name disambiguation. Scientometrics 93, 391–411 (2012). https://doi.org/10.1007/s11192-012-0681-1

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