In this system paper, we describe the DL-Learner framework, which supports supervised
machine learning using OWL and RDF for background knowledge representation. It can be …

Classical, two-valued, ontologies have been successfully applied to represent the
knowledge in many domains. However, it has been pointed out that they are not suitable in …

The following paper is an extended summary of the journal paper" DL-Learner A framework
for inductive learning on the Semantic Web". In this system paper, we describe the DL …

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 an OWL …

Given a target class T of an OWL 2 ontology, positive (and possibly negative) examples of T,
we address the problem of learning, viz. inducing, from the examples, fuzzy class inclusion …

One of the bottlenecks of the ontology construction process is the amount of work required
with various figures playing a role in it: domain experts contribute their knowledge that has to …

Currently, there is a great deal of well-founded explicit knowledge formalizing general
notions, such as time concepts and the part_of relation. Yet, it is often the case that instead …

The aim of this talk is to present a detailed, self-contained and comprehensive account of the
state of the art in representing and reasoning with structured fuzzy knowledge. Fuzzy …

Logics (DLs) are a family of logic-based Knowledge Representation (KR) formalisms, which
are particularly suitable for representing incomplete yet precise structured knowledge …

Abstract Fuzzy Description Logics (DLs) are logics that allow to deal with structured vague
knowledge. Although a relatively important amount of work has been carried out in the last …