TIB Leibniz Information Centre for Science and Technology, Welfengarten 1B, 30167, Hannover, Germany.
Don Chandler Entomological Collection, University of New Hampshire, Durham, NH, USA.
J Biomed Semantics. 2022 Jun 27;13(1):18. doi: 10.1186/s13326-022-00268-2.
In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look?
Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term.
We argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information-a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept.
We conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.
在生命科学领域数据呈指数级增长的时代，机器支持的方法变得越来越重要，因此需要 FAIR（可查找、可访问、可互操作、可重用）和 eScience 兼容的数据和元数据标准。本体论凭借其可查询的知识资源，在提供这些标准方面发挥着重要作用。不幸的是，生物医学本体论仅提供了回答“它是什么？”问题的本体定义，但缺乏回答“它看起来如何？”问题的方法相关的经验识别标准。
因此，生物医学本体论包含了结构类型的基础本体性质的知识，但通常缺乏足够的诊断知识来明确确定术语的参考。
我们认为，这是因为本体论术语通常是文本定义的，并被认为是本质主义的类别，而识别标准通常需要基于感知的定义，因为基于感知的内容更有效地记录和传达空间和时间信息——一图胜千言。因此，诊断知识通常必须被认为是聚类类别或模糊集。我们使用解剖学中的几个例子指出了诊断知识在解剖学研究中的重要性，并讨论了聚类类别和模糊集作为除本质主义类别之外，解剖学本体论中所需的分组概念的作用。在这种情况下，我们评估了生物类型概念的作用，并讨论了其作为未涵盖在本质主义类别概念中的分组的通用容器概念的功能。
我们得出结论，许多识别标准可以被概念化为基于文本的聚类类别，这些类别使用的术语反过来又基于基于感知的模糊集概念。最后，我们指出，只有当生物医学本体论除了本体论知识之外，还能够模拟相关的诊断知识，它们才能充分发挥潜力，为生命科学中 FAIR 和 eScience 兼容的数据和元数据标准的建立做出更大的贡献。
