References
- Amancio, D.R., Oliviera Jr., O.N., & Costa, L.D.F. (2015). Topological-collaborative approach for disambiguating authors’ names in collaborative networks. Scientometrics, 102(1), 465–485.
- Cota, R.G., Gonçalves, M.A., & Laender, A.H.F. (2007). A heuristic-based hierarchical clustering method for author name disambiguation in digital libraries. In Proceedings of the XXII Brazilian symposium on databases (pp. 20–34). João Pessoa, Paraiba, Brazil.
- 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 & Technology, 62(2), 257–269. doi:10.1002/asi. 21460
- Diesner, J., & Kim, J. (2016). Distortive Effects of Initial-Based Name Disambiguation on Measurements of Large-Scale Co-authorship Networks. Journal of the Association for Information Science and Technology, 67(6), 1446–1461.
- Ferreira, A., Goncalves, M.A., &Laender, A.H.F. (2012). A Brief Survey of Automatic Methods for Author Name Disambiguation. Sigmod Record, 41(2), 15–26.
- Gasparyan A.Y., Nurmashev B., Yessirkepov M., Endovitskiy D.A., Voronov A.A., & Kitas G.D. (2017). Researcher and Author Profiles: Opportunities, Advantages, and Limitations. J Korean Med Sci., 32(11), 1749–1756.
https://doi.org/10.3346/jkms.2017.32.11.1749 - Han, H., Giles, C.L., Zha, H., Li, C., & Tsioutsiouliklis, K. (2004). Two supervised learning approaches for name disambiguation in author citations. In Proceedings of the 4th ACM/IEEE-CS joint conference on digital libraries (pp. 296–305), Tuscon, USA.
- Haesun Park (2006). Georgia Tech. Retrieved from
https://www.cc.gatech.edu/~hpark - Hussein, I. & Asghar, S (2017). A survey of author name disambiguation techniques: 2010–2016. The Knowledge Engineering Review. 32. 10.1017/S0269888917000182.
- Iversen, E.J., Gulbrandsen, M., & Klitkou, A. (2007). A baseline for the impact of academic patenting legislation in Norway. Scientometrics, 70(2), 393–414.
- Lang, F., Chavarro, D. & Liu, Y. (2016). Can Automatic Classification Help to Increase Accuracy in Data Collection? Journal of Data and Information Science, 1(3), 42–58.
- Macroberts, M.H., & Macroberts, B.R. (1989). Problems of citation analysis: A critical review. Journal of the American Society for Information Science, 40, 342–349.
- ORCID Connecting Research and Researchers (2010). ORCID. Retrieved from
https://orcid.org - ResearcherID (2008). Thompson Reuters. Retrieved from
www.researcherid.com - ResearcherID (2011). Clarivate Analytics. Retrieved from
https://clarivate.com/products/researcherid - 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.
- Smalheiser, N.R., & Torvik, V.I. (2009). Author name disambiguation. In B. Cronin (Ed.), Annual review of information science and technology (Vol. 43). Maryland, USA: American Society for Information Science and Technology (ASIST).
- Song, Y., Huang, J., Councill, I.G., Li, J., & Giles, C.L. (2007). Efficient topic-based unsupervised name disambiguation. In Proceedings of the 7th ACM/IEEE joint conference on digital libraries (pp. 342–351). Vancouver, BC, Canada.
- Song, M., Kim, E.H.J., & Kim, H.J. (2015). Exploring author name disambiguation on PubMed-scale. Journal of Infometrics, 9(4), 924–941.
- Tang, L., & Walsh, J.P. (2010). Bibliometric fingerprints: name disambiguation based on approximate structure equivalence of cognitive maps. Scientometrics. doi: 10.1007/s11192-010-0196-6
- Who uses Scopus (2015). Elsevier. Retrieved from
https://www.elsevier.com/solutions/scopus/content - Wiktionary (2007). Wikimedia Foundation. Retrieved from
https://en.wiktionary.org/wiki/ Appendix: Korean_surnames. - Youtie, J., Carley, S., Porter, A.L. & Shapira, P. (2017). Tracking researchers and their outputs: new insights from ORCIDs. Scientometrics, 113(1), 437–453.
- Zhou, P. & Leydesdorff, L. (2006). The emergence of China as a leading nation in science. Research Policy, 35(1), 83–104.