We propose new innovative methods in order to reconstruct paradigmatic fields thanks to simple statistics over a scientific content database. We first define an asymmetric paradigmatic proximity between concepts which provides hierarchical structure over the set of concepts. We propose to implement overlapping categorization to describe paradigmatic fields as sets of concepts that may have several different usage and introduce a 2D embedding to represent these sets in a structured way. This enables to have a micro, meso and macro scale approach to our set of concepts. Concepts can also be dynamically clustered providing a high-level description of the evolution of the paradigmatic fields. We apply our set of methods on a case study from the Complex Systems Community through the mapping of the dynamics of more than 400 Complex Systems Science concepts indexed in a database of of several millions of journal papers.