The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies.

翻译：寻求获得一个感兴趣的领域知识的正式代表,吸引了具有不同背景的研究人员进入一个不同的领域,即“本体学学习”。我们强调为(半)预测描述逻辑(DL)的创建而提出的古典机器学习和数据挖掘方法,这些方法基于联合规则采矿、正式概念分析、感应逻辑编程、计算学理论和神经网络。我们概述了每一种方法以及这些方法如何适应于DL肿瘤学。最后,我们讨论了每一种方法在学习DL肿瘤学方面的优缺点和局限性。