<abstract xmlns="http://www.w3.org/1999/xhtml"><sec><h3>Purpose</h3><p>Our work seeks to overcome data quality issues related to incomplete author affiliation data in bibliographic records in order to support accurate and reliable measurement of international research collaboration (IRC).</p></sec><sec><h3>Design/methodology/approch</h3><p>We propose, implement, and evaluate a method that leverages the Web-based knowledge graph Wikidata to resolve publication affiliation data to particular countries. The method is tested with general and domain-specific data sets.</p></sec><sec><h3>Findings</h3><p>Our evaluation covers the magnitude of improvement, accuracy, and consistency. Results suggest the method is beneficial, reliable, and consistent, and thus a viable and improved approach to measuring IRC.</p></sec><sec><h3>Research limitations</h3><p>Though our evaluation suggests the method works with both general and domain-specific bibliographic data sets, it may perform differently with data sets not tested here. Further limitations stem from the use of the R programming language and R libraries for country identification as well as imbalanced data coverage and quality in Wikidata that may also change over time.</p></sec><sec><h3>Practical implications</h3><p>The new method helps to increase the accuracy in IRC studies and provides a basis for further development into a general tool that enriches bibliographic data using the Wikidata knowledge graph.</p></sec><sec><h3>Originality</h3><p>This is the first attempt to enrich bibliographic data using a peer-produced, Web-based knowledge graph like Wikidata.</p></sec></abstract>

PurposeOur work seeks to overcome data quality issues related to incomplete author affiliation data in bibliographic records in order to support accurate and reliable measurement of international research collaboration (IRC).Design/methodology/approchWe propose, implement, and evaluate a method that leverages the Web-based knowledge graph Wikidata to resolve publication affiliation data to particular countries. The method is tested with general and domain-specific data sets.FindingsOur evaluation covers the magnitude of improvement, accuracy, and consistency. Results suggest the method is beneficial, reliable, and consistent, and thus a viable and improved approach to measuring IRC.Research limitationsThough our evaluation suggests the method works with both general and domain-specific bibliographic data sets, it may perform differently with data sets not tested here. Further limitations stem from the use of the R programming language and R libraries for country identification as well as imbalanced data coverage and quality in Wikidata that may also change over time.Practical implicationsThe new method helps to increase the accuracy in IRC studies and provides a basis for further development into a general tool that enriches bibliographic data using the Wikidata knowledge graph.OriginalityThis is the first attempt to enrich bibliographic data using a peer-produced, Web-based knowledge graph like Wikidata.

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

Posts and Telecommunications Institute of Technology

Berlin School of Library and Information Science

Te Pūnaha Matatini - The New Zealand Centre of Research Excellence for Complex Systems and Networks

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

PurposeOur work seeks to overcome data quality issues related to incomplete author affiliation data in bibliographic records in order to support accurate and...

Features	ACM DL	MAG
Total works	182,791	212,689,976
Unique, co-authored, computer science works	121,672	557,730

Code	Results	ACM DL	MAG
C	Affiliations in co-authored works	384,672	853,482
C1	“NA”, “None”, etc values	52,454 (13.64%)	66,924 (7.84%)
C2	Identified	273,245 (71.02%)	643,678 (75.42%)
C2.1	Identified by string matching	236,100 (61.38%)	594,911 (69.70%)
C2.2	Identified by Wikidata	37,106 (9.65%)	48,767 (5.71%)
C3	Not identified (Other values)	59,012 (15.34%)	142,888 (16.74%)

	ACM DL	MAG
Mean	5.70%	5.42%
Standard error	0.69%	1.29%
Median	1.20%	0.91%
Mode	0%	0%
Standard deviation	8.10%	17.88%
Interquartile range	6.30%	10.18%
Count	137	192

	ACM DL	MAG
False match rate (FMR)	0 %	0 %
False non-match rate (FNMR)	73 %	75 %

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

Article Category: Research Paper

Published Online: Jul 09, 2020

Page range: 97 - 115

Received: Feb 01, 2020

Accepted: Jun 11, 2020

DOI: https://doi.org/10.2478/jdis-2020-0020

Keywords
International research collaboration measurement, Bibliographic data, Country identification, Knowledge graphs, Wikidata, Open data

© 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

Figure 2

Figure 3

Figure 4

Summary of data sets used.

Results of country identification.

Summary statistics of the method's results.

The accuracy of the method using Wikidata query.

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

Article Category: Research Paper

Published Online: Jul 09, 2020

Page range: 97 - 115

Received: Feb 01, 2020

Accepted: Jun 11, 2020

DOI: https://doi.org/10.2478/jdis-2020-0020

KeywordsInternational research collaboration measurement, Bibliographic data, Country identification, Knowledge graphs, Wikidata, Open data

© 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

Figure 2

Figure 3

Figure 4

Summary of data sets used.

Results of country identification.

Summary statistics of the method's results.

The accuracy of the method using Wikidata query.

Keywords
International research collaboration measurement, Bibliographic data, Country identification, Knowledge graphs, Wikidata, Open data