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

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: This is the first attempt to enrich bibliographic data using a peer-produced, Webbased knowledge graph like Wikidata.

The Computer Science Ontology:A Comprehensive Automatically-Generated Taxonomy of Research Areas

Ontologies of research areas are important tools for characterizing,exploring,and analyzing the research landscape.Some fields of research are comprehensively described by large-scale taxonomies,e.g.,MeSH in Biology and PhySH in Physics.Conversely,current Computer Science taxonomies are coarse-grained and tend to evolve slowly.For instance,the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012.In this paper,we introduce the Computer Science Ontology(CSO),a large-scale,automatically generated ontology of research areas,which includes about 14K topics and 162K semantic relationships.It was created by applying the Klink-2 algorithm on a very large data set of 16M scientific articles.CSO presents two main advantages over the alternatives:i)it includes a very large number of topics that do not appear in other classifications,and ii)it can be updated automatically by running Klink-2 on recent corpora of publications.CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions,such as classifying research publications,detecting research communities,and predicting research trends.To facilitate the uptake of CSO,we have also released the CSO Classifier,a tool for automatically classifying research papers,and the CSO Portal,a Web application that enables users to download,explore,and provide granular feedback on CSO.Users can use the portal to navigate and visualize sections of the ontology,rate topics and relationships,and suggest missing ones.The portal will support the publication of and access to regular new releases of CSO,with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.