To develop new methods in order to detect paradigmatic fields thanks to simple statistics over a scientific content database. We have defined an asymmetric paradigmatic proximity between concepts which provide hierarchical structure and we have tested our methods on a case study with a database of 20 000 000M article. We also propose overlapping categorisation to describe paradigmatic fields as sets of concepts that may have several different usages. Concepts can also be dynamically clustered provinding a high-level description of the evolution of the paradigmatic fields.
Domains of application :
Identification of paradigmatic fields defined as ordered keywords clusters. !!Required: * Indexation of a database : occurences and co-occurences of words on several time periods (e.g. years). NO direct access to documents required.