Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting

TL;DR

Fuzzy OWL-BOOST introduces a boosting-based method to learn fuzzy concept inclusion axioms in OWL ontologies, enabling automatic classification and reasoning of individuals with degrees of membership.

Contribution

It adapts the Real AdaBoost algorithm to fuzzy OWL, allowing the learning of fuzzy rules that can be directly integrated into Fuzzy OWL 2 for reasoning.

Findings

Effective learning of fuzzy concept inclusion axioms

Rules can be directly represented in Fuzzy OWL 2

Enables automatic classification with degrees of membership

Abstract

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.