With the expanding of the SemanticWeb and the availability
of numerous ontologies which provide domain background knowledge and
semantic descriptors to the data, the amount of semantic data is rapidly
growing. The data mining community is faced with a paradigm shift:
instead of mining the abundance of empirical data supported by the
background knowledge, the new challenge is to mine the abundance of
knowledge encoded in domain ontologies, constrained by the heuristics
computed from the empirical data collection. We address this challenge
by an approach, named semantic data mining, where domain ontologies
define the hypothesis search space, and the data is used as means of
constraining and guiding the process of hypothesis search and evaluation.
The use of prototype semantic data mining systems SEGS and g-SEGS is
demonstrated in a simple semantic data mining scenario and in two reallife
functional genomics scenarios of mining biological ontologies with the
support of experimental microarray data