Inter-Journal Knowledge Map using Citation Information and Subject Classification

Abstract

In this paper, citation data that is cross?referenced among journals is established using reference data in CrossRef. Using the established citation data, cited information is created and using DDC classification information, journals are classified. Using this inter?journal citation information, subject classification and time information, the relationship among the journals is analyzed. In other words, the relationship among frequently cited journals is expressed with DDC classification, and whether the subject classification is inter?related to journals and whether the subject journals are relevant are analyzed by year. As a result, it is shown that most journals are co?related and that technology (600) in particular is strongly co?related to natural science and mathematics (500) and social sciences (300). However, the analysis of journals by year shows that researches in the field of language (400) are conducted by heavily citing natural science and mathematics (500) but not the other way around with only a few citations after the year 2000. It is shown that there could be fusion among disciplines of no direct relations to each other.