  Finding inherent or processed links within a dataset allows to discover
potential knowledge. The main contribution of this article is to define a
global framework that enables optimal knowledge discovery by visually rendering
co-occurences (i.e. groups of linked data instances attached to a metadata
reference) - either inherently present or processed - from a dataset as facets.
Hypergraphs are well suited for modeling co-occurences since they support
multi-adicity whereas graphs only support pairwise relationships. This article
introduces an efficient navigation between different facets of an information
space based on hypergraph modelisation and visualisation.
