Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

• Published in: Journal of biomedical semantics Vol. 13; no. 1; pp. 18 - 27
• Main Authors: Vogt, Lars, Mikó, István, Bartolomaeus, Thomas
• Format: Journal Article
• Language: English
• Published: London BioMed Central 27.06.2022; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Anatomy; Bioinformatics; Biological Ontologies; Biological Science Disciplines; Biology; Biomedical ontology; Chemical elements; Cluster class; Clusters; Cognition & reasoning; Combinatorial Libraries; Computational Biology/Bioinformatics; Computational linguistics; Computer Appl. in Life Sciences; Controlled vocabularies; Criteria; Data Mining and Knowledge Discovery; Diagnosis; Diagnostic systems; Essentialistic class; FAIR data; Fuzzy set; Fuzzy sets; Human anatomy; Language; Language processing; Life sciences; Mathematics; Mathematics and Statistics; Medical research; Medicine, Experimental; Metadata; Morphology; Natural language; Natural language interfaces; Ontology; Perception; Phylogenetics; Questions; Recognition; Resource Description Framework-RDF; Semantics; Terminology; Web Ontology Language-OWL; Germany; Cluster class; Ontological definition; Recognition criteria; Diagnostic knowledge; Biomedical ontology; Anatomy; Fuzzy set; Ontological knowledge; FAIR data; Essentialistic class
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-022-00268-2

Background 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? questions. 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. Results 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. Conclusions 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.