Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued
  Boosting
  OWL ontologies are nowadays a quite popular way to describe structured
knowledge in terms of classes, relations among classes and class instances. In
this paper, given a target class T of an OWL ontology, we address the problem
of learning fuzzy concept inclusion axioms that describe sufficient conditions
for being an individual instance of T. To do so, we present Fuzzy OWL-BOOST
that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL
case. We illustrate its effectiveness by means of an experimentation. An
interesting feature is that the learned rules can be represented directly into
Fuzzy OWL 2. As a consequence, any Fuzzy OWL 2 reasoner can then be used to
automatically determine/classify (and to which degree) whether an individual
belongs to the target class T.
