Studying the cognitive relatedness between topics in the global science landscape: The case of Big Data research

Abstract

Taking Big Data research as a case study, this article intends to investigate the cognitive relatedness of research topics across the global science landscape to a focal topic. Several levels of cognitive relatedness are established depending on the citation distance between the citing publications and a core set of publications. The concept of citation generation is adopted for identifying and classifying other publications with different levels of relatedness to the core set. The micro publication-level classification system of Centre for Science and Technology Studies (CWTS) is applied for determining clusters of publication sets at the topic level. The overall cognitive relatedness of micro clusters to Big Data core publications are measured based on the mean citation generation of all the publications in corresponding clusters. In addition to the given clusters, this study also explores the ‘topics’ relatedness from a semantic point of view, by extracting high-frequency title terms of publications in each generation. Results show that data analysis methods and technologies are the topics with the strongest cognitive relatedness to Big Data research, while topics on physics and astronomy studies present the weakest relatedness. This approach allows assessment of relatedness between research topics by considering the citations distribution across multiple citation generations, and can provide useful insights to study and characterise topics with fuzzy boundaries or are difficult to delineate, thus representing a novel toolset relevant in the context of studying interdisciplinary research.