Why name ambiguity resolution matters for scholarly big data research

Jinseok Kim; Jana Diesner; Heejun Kim; Amirhossein Aleyasen; Hwan Min Kim

doi:10.1109/BigData.2014.7004345

Why name ambiguity resolution matters for scholarly big data research

Jinseok Kim, Jana Diesner, Heejun Kim, Amirhossein Aleyasen, Hwan Min Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper illustrates how data pre-processing choices about author name disambiguation can affect research findings about scholarly networks and hypotheses about underlying social mechanisms. We have analyzed three big scholarly datasets that were disambiguated algorithmically and via two common initial-based disambiguation methods; namely first-initial and all-initials disambiguation. The comparison of resulting bibliometric and network properties revealed that initial-disambiguation bears the prevalent risks of incorrectly merging author identities, underestimating the number of unique authors and inflating the average productivity and number of collaborators per author. The gaps between outcomes of name ambiguity resolution methods range from -4.23% to -87.36% per dataset for the number of unique authors, from 3.75% to 691.20% for average productivity, and from 5.06% to 285.28% for degree centrality for initial based methods compared to algorithmic disambiguation. This calls for special attention to data pre-processing choices in scholarly big data research.

Original language	English (US)
Title of host publication	Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
Editors	Wo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781479956654
DOIs	https://doi.org/10.1109/BigData.2014.7004345
State	Published - Jan 7 2015
Event	2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States Duration: Oct 27 2014 → Oct 30 2014

Publication series

Name	Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Other

Other	2nd IEEE International Conference on Big Data, IEEE Big Data 2014
Country/Territory	United States
City	Washington
Period	10/27/14 → 10/30/14

Keywords

Bibliometrics
Collaboration
Disambiguation
Network analysis

ASJC Scopus subject areas

Artificial Intelligence
Information Systems

Online availability

10.1109/BigData.2014.7004345

Library availability

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Kim, J., Diesner, J., Kim, H., Aleyasen, A., & Kim, H. M. (2015). Why name ambiguity resolution matters for scholarly big data research. In W. Chang, J. Huan, N. Cercone, S. Pyne, V. Honavar, J. Lin, X. T. Hu, C. Aggarwal, B. Mobasher, J. Pei, & R. Nambiar (Eds.), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 (pp. 1-6). [7004345] (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2014.7004345

Why name ambiguity resolution matters for scholarly big data research. / Kim, Jinseok; Diesner, Jana; Kim, Heejun et al.

Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. ed. / Wo Chang; Jun Huan; Nick Cercone; Saumyadipta Pyne; Vasant Honavar; Jimmy Lin; Xiaohua Tony Hu; Charu Aggarwal; Bamshad Mobasher; Jian Pei; Raghunath Nambiar. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1-6 7004345 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, J, Diesner, J, Kim, H, Aleyasen, A & Kim, HM 2015, Why name ambiguity resolution matters for scholarly big data research. in W Chang, J Huan, N Cercone, S Pyne, V Honavar, J Lin, XT Hu, C Aggarwal, B Mobasher, J Pei & R Nambiar (eds), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014., 7004345, Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2nd IEEE International Conference on Big Data, IEEE Big Data 2014, Washington, United States, 10/27/14. https://doi.org/10.1109/BigData.2014.7004345

Kim J, Diesner J, Kim H, Aleyasen A, Kim HM. Why name ambiguity resolution matters for scholarly big data research. In Chang W, Huan J, Cercone N, Pyne S, Honavar V, Lin J, Hu XT, Aggarwal C, Mobasher B, Pei J, Nambiar R, editors, Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. Institute of Electrical and Electronics Engineers Inc. 2015. p. 1-6. 7004345. (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). https://doi.org/10.1109/BigData.2014.7004345

Kim, Jinseok ; Diesner, Jana ; Kim, Heejun et al. / Why name ambiguity resolution matters for scholarly big data research. Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. editor / Wo Chang ; Jun Huan ; Nick Cercone ; Saumyadipta Pyne ; Vasant Honavar ; Jimmy Lin ; Xiaohua Tony Hu ; Charu Aggarwal ; Bamshad Mobasher ; Jian Pei ; Raghunath Nambiar. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 1-6 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).

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abstract = "This paper illustrates how data pre-processing choices about author name disambiguation can affect research findings about scholarly networks and hypotheses about underlying social mechanisms. We have analyzed three big scholarly datasets that were disambiguated algorithmically and via two common initial-based disambiguation methods; namely first-initial and all-initials disambiguation. The comparison of resulting bibliometric and network properties revealed that initial-disambiguation bears the prevalent risks of incorrectly merging author identities, underestimating the number of unique authors and inflating the average productivity and number of collaborators per author. The gaps between outcomes of name ambiguity resolution methods range from -4.23% to -87.36% per dataset for the number of unique authors, from 3.75% to 691.20% for average productivity, and from 5.06% to 285.28% for degree centrality for initial based methods compared to algorithmic disambiguation. This calls for special attention to data pre-processing choices in scholarly big data research.",

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AB - This paper illustrates how data pre-processing choices about author name disambiguation can affect research findings about scholarly networks and hypotheses about underlying social mechanisms. We have analyzed three big scholarly datasets that were disambiguated algorithmically and via two common initial-based disambiguation methods; namely first-initial and all-initials disambiguation. The comparison of resulting bibliometric and network properties revealed that initial-disambiguation bears the prevalent risks of incorrectly merging author identities, underestimating the number of unique authors and inflating the average productivity and number of collaborators per author. The gaps between outcomes of name ambiguity resolution methods range from -4.23% to -87.36% per dataset for the number of unique authors, from 3.75% to 691.20% for average productivity, and from 5.06% to 285.28% for degree centrality for initial based methods compared to algorithmic disambiguation. This calls for special attention to data pre-processing choices in scholarly big data research.

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Why name ambiguity resolution matters for scholarly big data research

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