  Formal Concept Analysis (FCA) is a mathematical theory based on the
formalization of the notions of concept and concept hierarchies. It has been
successfully applied to several Computer Science fields such as data
mining,software engineering, and knowledge engineering, and in many domains
like medicine, psychology, linguistics and ecology. For instance, it has been
exploited for the design, mapping and refinement of ontologies. In this paper,
we show how FCA can benefit from a given domain ontology by analyzing the
impact of a taxonomy (on objects and/or attributes) on the resulting concept
lattice. We willmainly concentrate on the usage of a taxonomy to extract
generalized patterns (i.e., knowledge generated from data when elements of a
given domain ontology are used) in the form of concepts and rules, and improve
navigation through these patterns. To that end, we analyze three generalization
cases and show their impact on the size of the generalized pattern set.
Different scenarios of simultaneous generalizations on both objects and
attributes are also discussed
