Document information
- Document Type:
- Manuscript
- Date:
- Received 15 July 2008 19:58 UTC; Posted 16 July 2008
- Subjects:
- Bioinformatics
- Abstract:
Increasingly, in data-intensive areas of the life sciences, experimental results are being described in algorithmically useful ways with the help of ontologies. Such ontologies are authored and maintained by scientists to support the retrieval, integration and analysis of their data. The proposition to be defended here is that ontologies of this type – the Gene Ontology (GO) being the most conspicuous example – are a part of science. Initial evidence for the truth of this proposition (which some will find self-evident) is the increasing recognition of the importance of empirically-based methods of evaluation to the ontology develop¬ment work being undertaken in support of scientific research. Ontologies created by scientists must, of course, be associated with implementations satisfying the requirements of software engineering. But the ontologies are not themselves engineering artifacts, and to conceive them as such brings grievous consequences. Rather, ontologies such as the GO are in different respects comparable to scientific theories, to scientific databases, and to scientific journal publications. Such a view implies a new conception of what is involved in the author¬ing, maintenance and application of ontologies in scientific contexts, and therewith also a new approach to the evaluation of ontologies and to the training of ontologists.
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- License:
- This document is licensed to the public under the Creative Commons Attribution 3.0 License
- How to cite this document:
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Smith, Barry. Ontology (Science). Available from Nature Precedings <http://hdl.handle.net/10101/npre.2008.2027.2> (2008)
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Other versions of this document in Nature Precedings
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Guenther Witzany on 21 July 2008 09:16 UTC
Ontologies are a modern version of metaphysics, i.e. sentences which look like scientific sentences but have non-scientific roots, e.g. believe-sentences. Without foundation and justification within a post-linguistic turn – paradigma this kind of bioinformatics will drift into a empiricism.