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

doi:10.2478/jdis-2020-0020

Abstract

Abstract:

Purpose: 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).

Design/methodology/approch: 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.

Findings: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.

Research limitations: 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.

Practical implications: 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.

Originality/value: This is the first attempt to enrich bibliographic data using a peer-produced, Web-based knowledge graph like Wikidata.

Key words: International research collaboration measurement, Bibliographic data, Country identification, Knowledge graphs, Wikidata, Open data

Xuan Nguyen, Ba, Jesse David Dinneen, and Markus Luczak-Roesch. "A Novel Method for Resolving and Completing Authors’ Country Affiliation Data in Bibliographic Records." Journal of Data and Information Science, vol.5, no.3, 2020, pp.97-115. DOI: 10.2478/jdis-2020-0020

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

	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