论文标题
模糊猫头鹰 - 促进：通过实现的增强学习模糊概念包含
Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued Boosting
论文作者
论文摘要
如今，猫头鹰本体是一种非常流行的方式来描述班级，班级和班级实例之间的结构化知识。在本文中，鉴于猫头鹰本体论的目标类T类T类，我们解决了学习模糊概念包含公理的问题，该问题描述了足够的条件，这些条件是T。这样做，我们提出了模糊的owl-boost，它依赖于改善（Fuzzy）Owl case的真实adaboost依赖于真正的adaboost。我们通过实验说明了其有效性。一个有趣的功能是，可以将学习的规则直接表示为模糊的猫头鹰2。因此，任何模糊的猫头鹰2推理器都可以自动确定/分类（以及在哪个程度上）个人是否属于目标类T。
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.
