Mining Research Topics Evolving Over Time Using a Diachronic Multi-source Approach

Abstract

The acquisition of new scientific knowledge and the evolution of the needs of the society regularly call into question the orientations of research. Means to recall and visualize these evolutions are thus necessary. The existing tools for research survey give only one fixed vision of the research activity, which does not allow performing tasks of dynamic topic mining. The objective of this paper is thus to propose a new incremental approach in order to follow the evolution of research themes and research groups for a scientific discipline given in terms of emergence or decline. These behaviors are detectable by various methods of filtering. However, our choice is made on the exploitation of neural clustering methods in a multi-view context. This new approach makes it possible to take into account the incremental and chronological aspect of information by opening the way to the detection of convergences and divergences of research themes and groups.