  Social scientists have shown an increasing interest in understanding the
structure of knowledge communities, and particularly the organization of
"epistemic communities", that is groups of agents sharing common knowledge
concerns. However, most existing approaches are based only on either social
relationships or semantic similarity, while there has been roughly no attempt
to link social and semantic aspects. In this paper, we introduce a formal
framework addressing this issue and propose a method based on Galois lattices
(or concept lattices) for categorizing epistemic communities in an automated
and hierarchically structured fashion. Suggesting that our process allows us to
rebuild a whole community structure and taxonomy, and notably fields and
subfields gathering a certain proportion of agents, we eventually apply it to
empirical data to exhibit these alleged structural properties, and successfully
compare our results with categories spontaneously given by domain experts.
