Semantic linkages in research information systems as a new data source for scientometric studies

• Published in: Scientometrics 2014-02, Vol.98 (2), p.927-943
• Main Authors: Parinov, Sergey, Kogalovsky, Mikhail
• Format: Article
• Language: English
• Subjects: Computer Science; Data collection; Datasets; Information Storage and Retrieval; Information systems; Library Science; Scientometrics; Semantics
• ISSN: 0138-9130; 1588-2861
• DOI: 10.1007/s11192-013-1108-3

A growing number of research information systems use a semantic linkage technique to represent in explicit mode information about relationships between elements of its content. This practice is coming nowadays to a maturity when already existed data on semantically linked research objects and expressed by this scientific relationships can be recognized as a new data source for scientometric studies. Recent activities to provide scientists with tools for expressing in a form of semantic linkages their knowledge, hypotheses and opinions about relationships between available information objects also support this trend. The study presents one of such activities performed within the Socionet research information system with a special focus on (a) taxonomy of scientific relationships, which can exist between research objects, especially between research outputs; and (b) a semantic segment of a research e-infrastructure that includes a semantic interoperability support, a monitoring of changes in linkages and linked objects, notifications and a new model of scientific communication, and at last—scientometric indicators built by processing of semantic linkages data. Based on knowledge what is a semantic linkage data and how it is stored in a research information system we propose an abstract computing model of a new data source. This model helps with better understanding what new indicators can be designed for scientometric studies. Using current semantic linkages data collected in Socionet we present some statistical experiments, including examples of indicators based on two data sets: (a) what objects are linked and (b) what scientific relationships (semantics) are expressed by the linkages.