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A Novel Method for Resolving and Completing Authors’ Country Affiliation Data in Bibliographic Records

Ba Xuan Nguyen

Ba Xuan Nguyen

  • School of Information Management, Victoria University of WellingtonWellington, New Zealand
  • Posts and Telecommunications Institute of TechnologyHo Chi Minh City,
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Nguyen, Ba Xuan
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Jesse David Dinneen

Jesse David Dinneen

  • School of Information Management, Victoria University of WellingtonWellington, New Zealand
  • Berlin School of Library and Information Science, Humboldt-Universität zu BerlinBerlin, Germany
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Dinneen, Jesse David
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Markus Luczak-Roesch

Markus Luczak-Roesch

  • School of Information Management, Victoria University of WellingtonWellington, New Zealand
  • Te Pūnaha Matatini - The New Zealand Centre of Research Excellence for Complex Systems and NetworksNew Zealand
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Luczak-Roesch, Markus
  
09 jul 2020
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Categoría del artículo: Research Paper
Publicado en línea: 09 jul 2020
Páginas: 97 - 115
Recibido: 01 feb 2020
Aceptado: 11 jun 2020
DOI: https://doi.org/10.2478/jdis-2020-0020
Palabras clave
© 2020 Ba Xuan Nguyen et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Abstract view of the different data retrieval and processing steps in our approach.
Abstract view of the different data retrieval and processing steps in our approach.

Figure 2

Comparing numbers of affiliation data unidentified using string matching with numbers of those identified using Wikidata query by year.
Comparing numbers of affiliation data unidentified using string matching with numbers of those identified using Wikidata query by year.

Figure 3

Average improvement (and outliers) of affiliations identified per country using the ACM Digital Library (left) and Microsoft Academic Graph (right) data sets.
Average improvement (and outliers) of affiliations identified per country using the ACM Digital Library (left) and Microsoft Academic Graph (right) data sets.

Figure 4

The number of affiliations identified for each country before and after using the Wikidata query, using ACM Digital Library and Microsoft Academic Graph data sets.
The number of affiliations identified for each country before and after using the Wikidata query, using ACM Digital Library and Microsoft Academic Graph data sets.

Summary of data sets used_

FeaturesACM DLMAG
Total works182,791212,689,976
Unique, co-authored, computer science works121,672557,730

Results of country identification_

CodeResultsACM DLMAG
CAffiliations in co-authored works384,672853,482
C1“NA”, “None”, etc values52,454 (13.64%)66,924 (7.84%)
C2Identified273,245 (71.02%)643,678 (75.42%)
C2.1Identified by string matching236,100 (61.38%)594,911 (69.70%)
C2.2Identified by Wikidata37,106 (9.65%)48,767 (5.71%)
C3Not identified (Other values)59,012 (15.34%)142,888 (16.74%)

Summary statistics of the method's results_

ACM DLMAG
Mean5.70%5.42%
Standard error0.69%1.29%
Median1.20%0.91%
Mode0%0%
Standard deviation8.10%17.88%
Interquartile range6.30%10.18%
Count137192

The accuracy of the method using Wikidata query_

ACM DLMAG
False match rate (FMR)0 %0 %
False non-match rate (FNMR)73 %75 %
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Temas de la revista:
Informática, Tecnologías de la información, Gestión de proyectos, Bases de datos y minería de datos
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