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 ontology and a target class T, we address the problem of learning fuzzy concept inclusion axioms that describe sufficient conditions for being an individual instance of T (and to which degree). To do so, we present Fuzzy OWL-Boost that relies on the $\mathbb{R}$eal AdaBoost boosting algorithm adapted to the (fuzzy) OWL case. We illustrate its effectiveness by means of an experimentation with several ontologies.

中文翻译：

---

Fuzzy OWL-Boost：通过实值提升学习模糊概念包含

OWL 本体现在是一种非常流行的方式，用于根据类、类之间的关系和类实例来描述结构化知识。在本文中，给定 OWL 本体和目标类T，我们解决了学习模糊概念包含公理的问题，这些公理描述了作为T的单个实例（以及程度）的充分条件。为此，我们提出了Fuzzy OWL-Boost，它依赖于$\mathbb{R}$eal AdaBoost 增强算法适用于（模糊）OWL 情况。我们通过对几个本体的实验来说明它的有效性。