Ontology learning, particularly axiom learning, is a challenging task that focuses on building expressive and decidable ontologies. The literature proposes several research efforts aimed to resolve the complexities inherent in axiom and rule learning, which seeks to automatically infer logical constructs from diverse data sources. The goal of this paper is to conduct a comprehensive review of existing work in this domain. It aims to critically analyze the contributions and limitations of current approaches, providing a clear understanding of the state-of-the-art and identifying areas where further research is needed.

中文翻译：

---

面向表现性的本体学习：一项调查


本体学习，尤其是公理学习，是一项具有挑战性的任务，专注于构建富有表现力和可决定性的本体论。文献提出了几项研究工作，旨在解决公理和规则学习中固有的复杂性，它试图从不同的数据源中自动推断逻辑结构。本文的目标是对该领域的现有工作进行全面审查。它旨在批判性地分析当前方法的贡献和局限性，提供对最新技术的清晰理解，并确定需要进一步研究的领域。