The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests organising ontologies in levels, where domain specific (low-level) ontologies are grounded in domain independent high-level ontologies (i.e., foundational ontologies). In this level-based organisation, foundational ontologies work as translators of intended meaning, thus improving interoperability. Despite their considerable acceptance in biomedical research, there are very few studies testing foundational ontologies. This paper describes a systematic literature mapping that was conducted to understand how foundational ontologies are used in biomedical research and to find empirical evidence supporting their claimed (dis)advantages. From a set of 79 selected papers, we identified that foundational ontologies are used for several purposes: ontology construction, repair, mapping, and ontology-based data analysis. Foundational ontologies are claimed to improve interoperability, enhance reasoning, speed up ontology development and facilitate maintainability. The complexity of using foundational ontologies is the most commonly cited downside. Despite being used for several purposes, there were hardly any experiments (1 paper) testing the claims for or against the use of foundational ontologies. In the subset of 49 papers that describe the development of an ontology, it was observed a low adherence to ontology construction (16 papers) and ontology evaluation formal methods (4 papers). Our findings have two main implications. First, the lack of empirical evidence about the use of foundational ontologies indicates a need for evaluating the use of such artefacts in biomedical research. Second, the low adherence to formal methods illustrates how the field could benefit from a more systematic approach when dealing with the development and evaluation of ontologies. The understanding of how foundational ontologies are used in the biomedical field can drive future research towards the improvement of ontologies and, consequently, data FAIRness. The adoption of formal methods can impact the quality and sustainability of ontologies, and reusing these methods from other fields is encouraged.

中文翻译：

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基础本体论在生物医学研究中的应用


FAIR 原则建议使用受控词汇（例如本体）来定义数据和元数据概念。目前，本体是按照不同的方法建模的，有时描述相同概念的相互冲突的定义，这可能会影响互操作性。为了解决这个问题，先前的文献建议按级别组织本体，其中特定领域（低级别）本体基于领域独立的高级本体（即基础本体）。在这种基于级别的组织中，基础本体充当预期含义的翻译者，从而提高互操作性。尽管它们在生物医学研究中得到了相当多的认可，但测试基础本体论的研究却很少。本文描述了系统的文献映射，旨在了解基础本体论如何在生物医学研究中使用，并找到支持其声称的（缺点）优势的经验证据。从 79 篇精选论文中，我们发现基础本体可用于多种目的：本体构建、修复、映射和基于本体的数据分析。基础本体据称可以提高互操作性、增强推理、加速本体开发并促进可维护性。使用基础本体的复杂性是最常被提及的缺点。尽管被用于多种目的，但几乎没有任何实验（一篇论文）来测试支持或反对使用基础本体的主张。在描述本体发展的 49 篇论文中，观察到对本体构建（16 篇论文）和本体评估形式化方法（4 篇论文）的遵守率较低。我们的发现有两个主要意义。 首先，缺乏关于基础本体论使用的经验证据表明需要评估此类人工制品在生物医学研究中的使用。其次，对正式方法的遵守程度较低，这表明该领域在处理本体的开发和评估时如何可以从更系统的方法中受益。了解基础本体如何在生物医学领域使用可以推动未来的研究朝着改进本体的方向发展，从而提高数据的公平性。采用形式化方法会影响本体的质量和可持续性，并且鼓励重用其他领域的这些方法。