  The Folksodriven framework makes it possible for data scientists to define an
ontology environment where searching for buried patterns that have some kind of
predictive power to build predictive models more effectively. It accomplishes
this through an abstractions that isolate parameters of the predictive modeling
process searching for patterns and designing the feature set, too. To reflect
the evolving knowledge, this paper considers ontologies based on folksonomies
according to a new concept structure called "Folksodriven" to represent
folksonomies. So, the studies on the transformational regulation of the
Folksodriven tags are regarded to be important for adaptive folksonomies
classifications in an evolving environment used by Intelligent Systems to
represent the knowledge sharing. Folksodriven tags are used to categorize
salient data points so they can be fed to a machine-learning system and
"featurizing" the data.
