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

arXiv: https://arxiv.org/abs/2008.05297 · HTML: https://mlsanity.com/paper/2008.05297/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [PN-OWL: A Two Stage Algorithm to Learn Fuzzy Concept Inclusions from OWL Ontologies](https://mlsanity.com/paper/2303.07192/) (2023)
- [Incremental Bootstrapping and Classification of Structured Scenes in a Fuzzy Ontology](https://mlsanity.com/paper/2404.11744/) (2024)
- [Fuzzy Quantification over OWL Ontologies and Knowledge Graphs](https://mlsanity.com/paper/2606.25778/) (2026)
- [A Fuzzy Rule-based Neuro-Symbolic Approach for Pipe Severity Prediction in Sewer Networks](https://mlsanity.com/paper/2607.28481/) (2026)

## Abstract (arXiv)

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.