Quantitative Estimation of Trends in Artificial Intelligence Research: A Study of Bradford Distributions using Leimkuhler Model

Abstract

The ubiquitous applications of Artificial Intelligence (AI) in various domains of human life have resulted in a phenomenal increase in AI research. The research output in AI has grown rapidly during the last decade. While some scientometric studies have noted this growth in publications, there are virtually no studies that could characterize the growth in publications in terms of the increase in domains and journals in which AI research is being carried out and published. This article makes an attempt to fill this research gap by using the Leimkuhler model of Bradford’s law of productivity to produce quantitative estimates of AI research publishing. Publications indexed in Web of Science for the period 2011 to 2020 are used for analysis. The analysis explains the variation in the corpus of AI research using productivity distribution and its characteristics. The quantitative findings support the idea that AI research has not only increased in volume but also in terms of applications to a wider list of areas.

Keywords: Artificial Intelligence; Bradford Law of Scattering; Leimkuhler Model; Journal Productivity Distribution