In recent years, several standard ontologies have been developed to maximise semantic interoperability in different domains; such standard ontologies ensure quality and integrity when describing a domain. Therefore, mechanisms to guarantee that developers build ontologies that conform to such standards are needed. However, while in fields such as Software Engineering or industry, conformance testing plays an essential role during product development, in the Ontology Engineering field there is a lack of techniques for this type of testing. This work introduces an ontology conformance testing method to analyse conformance between an ontology and a standard based on the standard requirements. Grounded on this method, the work also presents a minimum common knowledge identification method for analysing how a group of standards covers a particular domain and for identifying whether there are conflicts between them. This work has been validated by analysing the conformance between an ontology network and a set of standards on the Internet of Things domain, and by analysing the minimum common knowledge between such standards. This analysis shows that the conformance between ontologies and standards is mostly related to definition of classes. Furthermore, the analysis shows that although the analysed standards are related to the same domain, they are created to describe different areas of concern and, thus, there is a minimum overlap between them. Finally, it was concluded that the quality of the conformance analysis depends on the quality of the requirements specification: the more precise the requirements, the more precise the analysis between ontologies and standards.

近年来，已经开发了几种标准本体，以最大化不同领域中的语义互操作性。此类标准本体在描述域时可确保质量和完整性。因此，需要一种机制来确保开发人员构建符合此类标准的本体。但是，尽管在软件工程或工业等领域，一致性测试在产品开发过程中起着至关重要的作用，但在本体工程领域，却缺乏用于此类测试的技术。这项工作介绍了一种本体一致性测试方法，用于根据标准要求分析本体和标准之间的一致性。基于这种方法，该工作还提出了一种最低限度的常识识别方法，用于分析一组标准如何涵盖特定领域并识别标准之间是否存在冲突。通过分析本体网络与物联网域上的一组标准之间的一致性，以及通过分析这些标准之间的最小公知知识，已经验证了这项工作。分析表明，本体和标准之间的一致性主要与类的定义有关。此外，分析表明，尽管所分析的标准与同一领域相关，但创建它们是为了描述不同的关注领域，因此，它们之间的重叠最小。最后，