Abstract
Abstract
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.
Funder
Deutsche Forschungsgemeinschaft
Leibniz-Gemeinschaft
Horizon 2020
Technische Informationsbibliothek (TIB)
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Health Informatics,Computer Science Applications,Information Systems
Reference127 articles.
1. Gray J. Jim Gray on eScience: A Transformed Scientific Method. In: Hey T, Tansley S, Tolle K, editors. The Fourth Paradigm: Data-Intensive Scientific Discoveries. Redmond: Microsoft Research; 2009. p. xvii–xxi.
2. Wang X, Gorlitsky R, Almeida JS. From XML to RDF: how semantic web technologies will change the design of “omic” standards. Nat Biotechnol. 2005;23(9):1099–103. https://doi.org/10.1038/nbt1139.
3. Vogt L. eScience and the need for data standards in the life sciences: in pursuit of objectivity rather than truth. Syst Biodivers. 2013;11(3):257–70. https://doi.org/10.1080/14772000.2013.818588.
4. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data. 2016;3(1):160018. https://doi.org/10.1038/sdata.2016.18.
5. Snodgrass RE. Anatomy and morphology. J N Y Entomol Soc. 1951;59(2):71–3.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献