Ontologies are at the core of the semantic web. As knowledge bases, they are very useful resources for many artificial intelligence applications. Ontology learning, as a research area, proposes techniques to automate several tasks of the ontology construction process to simplify the tedious work of manually building ontologies. In this paper we present the state of the art of this field. Different classes of approaches are covered (linguistic, statistical, and machine learning), including some recent ones (deep-learning-based approaches). In addition, some relevant solutions (frameworks), which offer strategies and built-in methods for ontology learning, are presented. A descriptive summary is made to point out the capabilities of the different contributions based on criteria that have to do with the produced ontology components and the degree of automation. We also highlight the challenge of evaluating ontologies to make them reliable, since it is not a trivial task in this field; it actually represents a research area on its own. Finally, we identify some unresolved issues and open questions.

本体是语义网的核心。作为知识库，它们是许多人工智能应用程序非常有用的资源。本体学习作为研究领域，提出了使本体构建过程的多个任务自动化的技术，以简化手动构建本体的繁琐工作。在本文中，我们介绍了该领域的最新技术。涵盖了不同类别的方法（语言，统计和机器学习），包括一些最近的方法（基于深度学习的方法）。此外，还介绍了一些相关的解决方案（框架），这些解决方案提供了用于本体学习的策略和内置方法。基于与所产生的本体组件和自动化程度有关的标准，进行了描述性摘要以指出不同贡献的功能。我们还强调了评估本体以使其可靠的挑战，因为在该领域这不是一件容易的事。它实际上代表了一个研究领域。最后，我们找出一些未解决的问题和未解决的问题。