Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting
Cardillo F. A., Straccia U.
Fuzzy Logic Description Logics OWL 2 Machine Learning AdaBoost
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.
Source: Research report, pp.1–26, 2020
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@techreport{oai:it.cnr:prodotti:428576,
title = {Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting},
author = {Cardillo F. A. and Straccia U.},
institution = {Research report, pp.1–26, 2020},
year = {2020}
}
0000-0003-0940-4768