Understanding the evolution of a scientific field by clustering and visualizing knowledge graphs

The process of tracking the evolution of a scientific field is arduous. It allows researchers to understand trends in areas of science and predict how they may evolve. Nowadays, most of the automated mechanisms developed to assist researchers in this process do not consider the content of articles to identify changes in its structure, only the articles metadata. These methods are not suited to easily assist researchers to study the concepts that compose an area and its evolution. In this article, we propose a method to track the evolution of a scientific field at a concept-level. Our method structures a scientific field using two knowledge graphs, representing distinct periods of the studied field. Then, it clusters them and identifies correspondent clusters between the knowledge graphs, representing the same sub-areas in distinct time periods. Our solution enables to compare the corresponding clusters, tracking their evolution. We apply and experiment our method in two case studies concerning the Artificial Intelligence and the Biotechnology fields. Findings indicate befitting results regarding the way their evolution can be assessed with our implemented software tool. From our analyses, we perceived evolution in broader sub-areas of a scientific field, as the growth of the "Convolutional Neural Network" area from 2006; to specific ones, as the decrease of researches using mice to study BRAF-mutation lung cancer from 2018. This work contributes with the development of a web application with interactive user interfaces to assist researchers in representing, analyzing, and tracking the evolution of scientific fields at a concept-level.