  Building taxonomies is often a significant part of building an ontology, and
many attempts have been made to automate the creation of such taxonomies from
relevant data. The idea in such approaches is either that relevant definitions
of the intension of concepts can be extracted as patterns in the data (e.g. in
formal concept analysis) or that their extension can be built from grouping
data objects based on similarity (clustering). In both cases, the process leads
to an automatically constructed structure, which can either be too coarse and
lacking in definition, or too fined-grained and detailed, therefore requiring
to be refined into the desired taxonomy. In this paper, we explore a method
that takes inspiration from both approaches in an iterative and interactive
process, so that refinement and definition of the concepts in the taxonomy
occur at the time of identifying those concepts in the data. We show that this
method is applicable on a variety of data sources and leads to taxonomies that
can be more directly integrated into ontologies.
