Measuring cognitive distance between publication portfolios. Issue 2 (May 2017)

• Record Type: Journal Article
• Authors: Rousseau, Ronald; Guns, Raf; Rahman, A.I.M. Jakaria; Engels, Tim C.E.
• Is Part Of: Journal of informetrics. Volume 11:Issue 2(2017:Apr.)
• Page Start: 583
• Page End: 594
• Publication Date: 2017-05
• Subjects: Cognitive distances -- Barycenters -- Similarity matrices -- Similarity-adapted publication vectors -- Weighted cosine similarity -- Bootstrapping -- Research expertise; Library statistics -- Periodicals; Information science -- Statistical methods -- Periodicals; Bibliometrics -- Periodicals; Bibliothèques -- Statistiques -- Périodiques; Sciences de l'information -- Méthodes statistiques -- Périodiques; Bibliométrie -- Périodiques; 020.727
• DOI: 10.1016/j.joi.2017.03.001
• Languages: English
• ISSNs: 1751-1577

Abstract

Highlights: Cognitive distance between publication portfolios of scientific units is determined. The importance of scale invariance in determining cognitive distance is explained. Two similarity-based methods in N dimensions are proposed. Low dimensional and N-dimensional methods are compared in a small case study. Abstract: We study the problem of determining the cognitive distance between the publication portfolios of two units. In this article we provide a systematic overview of five different methods (a benchmark Euclidean distance approach, distance between barycenters in two and in three dimensions, distance between similarity-adapted publication vectors, and weighted cosine similarity) to determine cognitive distances using publication records. We present a theoretical comparison as well as a small empirical case study. Results of this case study are not conclusive, but we have, mainly on logical grounds, a small preference for the method based on similarity-adapted publication vectors.