  OWL ontologies are 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, with a focus on ontologies with
real- and boolean-valued data properties, we address the problem of learning
graded fuzzy concept inclusion axioms with the aim of describing enough
conditions for being an individual classified as instance of the class T. To do
so, we present PN-OWL that is a two-stage learning algorithm made of a P-stage
and an N-stage. Roughly, in the P-stage the algorithm tries to cover as many
positive examples as possible (increase recall), without compromising too much
precision, while in the N-stage, the algorithm tries to rule out as many false
positives, covered by the P-stage, as possible. PN-OWL then aggregates the
fuzzy inclusion axioms learnt at the P-stage and the N-stage by combining them
via aggregation functions to allow for a final decision whether an individual
is instance of T or not. We also illustrate its effectiveness by means of an
experimentation. An interesting feature is that fuzzy datatypes are built
automatically, the learnt fuzzy concept inclusions can be represented directly
into Fuzzy OWL 2 and, thus, 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 or not.
