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Scientometrics

, Volume 118, Issue 1, pp 253–280 | Cite as

Generating automatically labeled data for author name disambiguation: an iterative clustering method

  • Jinseok KimEmail author
  • Jinmo Kim
  • Jason Owen-Smith
Article

Abstract

To train algorithms for supervised author name disambiguation, many studies have relied on hand-labeled truth data that are very laborious to generate. This paper shows that labeled data can be automatically generated using information features such as email address, coauthor names, and cited references that are available from publication records. For this purpose, high-precision rules for matching name instances on each feature are decided using an external-authority database. Then, selected name instances in target ambiguous data go through the process of pairwise matching based on the rules. Next, they are merged into clusters by a generic entity resolution algorithm. The clustering procedure is repeated over other features until further merging is impossible. Tested on 26 K instances out of the population of 228 K author name instances, this iterative clustering produced accurately labeled data with pairwise F1 = 0.99. The labeled data represented the population data in terms of name ethnicity and co-disambiguating name group size distributions. In addition, trained on the labeled data, machine learning algorithms disambiguated 24 K names in test data with performance of pairwise F1 = 0.90–0.92. Several challenges are discussed for applying this method to resolving author name ambiguity in large-scale scholarly data.

Keywords

Author name disambiguation Entity resolution Labeled data Gold standard Supervised machine learning 

Notes

Acknowledgements

This work was supported by Grants from the National Science Foundation (#1,561,687 and #1535370), the Alfred P. Sloan Foundation and the Ewing Marion Kauffman Foundation. We would like to thank anonymous reviewers for their helpful comments.

References

  1. Bornmann, L., & Mutz, R. (2015). Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology, 66(11), 2215–2222.  https://doi.org/10.1002/asi.23329.CrossRefGoogle Scholar
  2. Cota, R. G., Ferreira, A. A., Nascimento, C., Gonçalves, M. A., & Laender, A. H. F. (2010). An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations. Journal of the American Society for Information Science and Technology, 61(9), 1853–1870.  https://doi.org/10.1002/asi.21363.CrossRefGoogle Scholar
  3. D’Angelo, C. A., Giuffrida, C., & Abramo, G. (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, 62(2), 257–269.  https://doi.org/10.1002/asi.21460.CrossRefGoogle Scholar
  4. Ferreira, A. A., Gonçalves, M. A., & Laender, A. H. F. (2012). A brief survey of automatic methods for author name disambiguation. Sigmod Record, 41(2), 15–26.CrossRefGoogle Scholar
  5. Ferreira, A. A., Veloso, A., Gonçalves, M. A., & Laender, A. H. F. (2014). Self-training author name disambiguation for information scarce scenarios. Journal of the Association for Information Science and Technology, 65(6), 1257–1278.  https://doi.org/10.1002/asi.22992.CrossRefGoogle Scholar
  6. Gomide, J., Kling, H., & Figueiredo, D. (2017). Name usage pattern in the synonym ambiguity problem in bibliographic data. Scientometrics, 112(2), 747–766.  https://doi.org/10.1007/s11192-017-2410-2.CrossRefGoogle Scholar
  7. Haak, L. L., Fenner, M., Paglione, L., Pentz, E., & Ratner, H. (2012). ORCID: A system to uniquely identify researchers. Learned Publishing, 25(4), 259–264.  https://doi.org/10.1087/20120404.CrossRefGoogle Scholar
  8. Han, H., Giles, L., Zha, H., Li, C., & Tsioutsiouliklis, K. (2004). Two supervised learning approaches for name disambiguation in author citations. In Proceedings of the fourth ACM/IEEE joint conference on digital libraries (pp. 296–305).  https://doi.org/10.1145/996350.996419.
  9. Han, H., Xu, W., Zha, H., & Giles, C. L. (2005a). A hierarchical naive Bayes mixture model for name disambiguation in author citations. In Proceedings of the 2005 ACM symposium on Applied computing—SAC ‘05. Santa Fe, New Mexico.Google Scholar
  10. Han, H., Zha, H. Y., & Giles, C. L. (2005b). Name disambiguation spectral in author citations using a K-way clustering method. In Proceedings of the 5th ACM/IEEE joint conference on digital libraries, proceedings (pp. 334–343).  https://doi.org/10.1145/1065385.1065462.
  11. Kang, I. S., Kim, P., Lee, S., Jung, H., & You, B. J. (2011). Construction of a large-scale test set for author disambiguation. Information Processing and Management, 47(3), 452–465.  https://doi.org/10.1016/j.ipm.2010.10.001.CrossRefGoogle Scholar
  12. Kim, J. (2018). Evaluating author name disambiguation for digital libraries: A case of DBLP. Scientometrics, 116(3), 1867–1886.  https://doi.org/10.1007/s11192-018-2824-5.CrossRefGoogle Scholar
  13. Kim, J., & Diesner, J. (2016). Distortive effects of initial-based name disambiguation on measurements of large-scale coauthorship networks. Journal of the Association for Information Science and Technology, 67(6), 1446–1461.  https://doi.org/10.1002/asi.23489.CrossRefGoogle Scholar
  14. Kim, J., & Kim, J. (2018). The impact of imbalanced training data on machine learning for author name disambiguation. Scientometrics, 117(1), 511–526.  https://doi.org/10.1007/s11192-018-2865-9.CrossRefGoogle Scholar
  15. Lerchenmueller, M. J., & Sorenson, O. (2016). Author disambiguation in PubMed: Evidence on the precision and recall of author-ity among NIH-funded scientists. PLoS ONE, 11(7), e0158731.  https://doi.org/10.1371/journal.pone.0158731.CrossRefGoogle Scholar
  16. Levin, M., Krawczyk, S., Bethard, S., & Jurafsky, D. (2012). Citation-based bootstrapping for large-scale author disambiguation. Journal of the American Society for Information Science and Technology, 63(5), 1030–1047.  https://doi.org/10.1002/asi.22621.CrossRefGoogle Scholar
  17. Liu, W., Islamaj Dogan, R., Kim, S., Comeau, D. C., Kim, W., Yeganova, L., et al. (2014). Author name disambiguation for PubMed. Journal of the Association for Information Science and Technology, 65(4), 765–781.  https://doi.org/10.1002/asi.23063.CrossRefGoogle Scholar
  18. Louppe, G., Al-Natsheh, H. T., Susik, M., & Maguire, E. J. (2016). Ethnicity sensitive author disambiguation using semi-supervised learning. Knowledge Engineering and Semantic Web, Kesw, 2016(649), 272–287.  https://doi.org/10.1007/978-3-319-45880-9_21.Google Scholar
  19. Milojević, S. (2013). Accuracy of simple, initials-based methods for author name disambiguation. Journal of Informetrics, 7(4), 767–773.CrossRefGoogle Scholar
  20. Müller, M. C., Reitz, F., & Roy, N. (2017). Data sets for author name disambiguation: an empirical analysis and a new resource. Scientometrics, 111(3), 1467–1500.  https://doi.org/10.1007/s11192-017-2363-5.CrossRefzbMATHGoogle Scholar
  21. Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 404–409.  https://doi.org/10.1073/pnas.021544898.MathSciNetCrossRefzbMATHGoogle Scholar
  22. Porter, M. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137.CrossRefGoogle Scholar
  23. Qian, Y., Zheng, Q., Sakai, T., Ye, J., & Liu, J. (2015). Dynamic author name disambiguation for growing digital libraries. Information Retrieval Journal, 18(5), 379–412.  https://doi.org/10.1007/s10791-015-9261-3.CrossRefGoogle Scholar
  24. Santana, A. F., Gonçalves, M. A., Laender, A. H. F., & Ferreira, A. A. (2015). On the combination of domain-specific heuristics for author name disambiguation: The nearest cluster method. International Journal on Digital Libraries, 16(3–4), 229–246.  https://doi.org/10.1007/s00799-015-0158-y.CrossRefGoogle Scholar
  25. Schulz, C., Mazloumian, A., Petersen, A. M., Penner, O., & Helbing, D. (2014). Exploiting citation networks for large-scale author name disambiguation. EPJ Data Science.  https://doi.org/10.1140/epjds/s13688-014-0011-3.Google Scholar
  26. Schulz, J. (2016). Using Monte Carlo simulations to assess the impact of author name disambiguation quality on different bibliometric analyses. Scientometrics, 107(3), 1283–1298.  https://doi.org/10.1007/s11192-016-1892-7.CrossRefGoogle Scholar
  27. Shin, D., Kim, T., Choi, J., & Kim, J. (2014). Author name disambiguation using a graph model with node splitting and merging based on bibliographic information. Scientometrics, 100(1), 15–50.  https://doi.org/10.1007/s11192-014-1289-4.CrossRefGoogle Scholar
  28. Smalheiser, N. R., & Torvik, V. I. (2009). Author name disambiguation. Annual Review of Information Science and Technology, 43, 287–313.CrossRefGoogle Scholar
  29. Song, M., Kim, E. H. J., & Kim, H. J. (2015). Exploring author name disambiguation on PubMed-scale. Journal of Informetrics, 9(4), 924–941.  https://doi.org/10.1016/j.joi.2015.08.004.CrossRefGoogle Scholar
  30. Strotmann, A., & Zhao, D. Z. (2012). Author name disambiguation: What difference does it make in author-based citation analysis? Journal of the American Society for Information Science and Technology, 63(9), 1820–1833.  https://doi.org/10.1002/asi.22695.CrossRefGoogle Scholar
  31. Torvik, V. I. (2015). MapAffil: A Bibliographic tool for mapping author affiliation strings to cities and their geocodes worldwide. D-Lib magazine: The magazine of the Digital Library Forum.  https://doi.org/10.1045/november2015-torvik.Google Scholar
  32. Torvik, V. I., & Agarwal, S. (2016). Ethnea: An instance-based ethnicity classifier based on geo-coded author names in a large-scale bibliographic database. In Paper presented at the library of congress international symposium on science of science, Washington, DC, USA. http://hdl.handle.net/2142/88927.
  33. Torvik, V. I., & Smalheiser, N. R. (2009). Author name disambiguation in MEDLINE. ACM Transactions on Knowledge Discovery from Data.  https://doi.org/10.1145/1552303.1552304.Google Scholar
  34. Treeratpituk, P., & Giles, C. L. (2009). Disambiguating authors in academic publications using random forests. In Proceedings of the 2009 ACM/IEEE joint conference on digital libraries (pp. 39–48).Google Scholar
  35. Vogel, T., Heise, A., Draisbach, U., Lange, D., & Naumann, F. (2014). Reach for gold: An annealing standard to evaluate duplicate detection results. Journal of Data and Information Quality, 5(1–2), 1–25.  https://doi.org/10.1145/2629687.CrossRefGoogle Scholar
  36. Wang, J., Berzins, K., Hicks, D., Melkers, J., Xiao, F., & Pinheiro, D. (2012). A boosted-trees method for name disambiguation. Scientometrics, 93(2), 391–411.  https://doi.org/10.1007/s11192-012-0681-1.CrossRefGoogle Scholar
  37. Wang, X., Tang, J., Cheng, H., & Yu, P. S. (2011). ADANA: Active name disambiguation. In Paper presented at the 2011 IEEE 11th international conference on data mining. http://ieeexplore.ieee.org/document/6137284/.
  38. Whang, S. E., Menestrina, D., Koutrika, G., Theobald, M., & Garcia-Molina, H. (2009). Entity resolution with iterative blocking. In Proceedings of the 2009 ACM SIGMOD international conference on management of data. Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  1. 1.Institute for Research on Innovation and Science, Survey Research Center, Institute for Social ResearchUniversity of MichiganAnn ArborUSA
  2. 2.School of Information SciencesUniversity of Illinois at Urbana-ChampaignChampaignUSA
  3. 3.Department of Sociology, Institute for Social ResearchUniversity of MichiganAnn ArborUSA

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