A document-centric approach for developing the tolAPC ontology.

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A document-centric approach for developing the tolAPC ontology.

J Biomed Semantics. 2017 Nov 28;8(1):54

Authors: Blfgeh A, Warrender J, Hilkens CMU, Lord P

BACKGROUND: There are many challenges associated with ontology building, as the process often touches on many different subject areas; it needs knowledge of the problem domain, an understanding of the ontology formalism, software in use and, sometimes, an understanding of the philosophical background. In practice, it is very rare that an ontology can be completed by a single person, as they are unlikely to combine all of these skills. So people with these skills must collaborate. One solution to this is to use face-to-face meetings, but these can be expensive and time-consuming for teams that are not co-located. Remote collaboration is possible, of course, but one difficulty here is that domain specialists use a wide-variety of different “formalisms” to represent and share their data – by the far most common, however, is the “office file” either in the form of a word-processor document or a spreadsheet. Here we describe the development of an ontology of immunological cell types; this was initially developed by domain specialists using an Excel spreadsheet for collaboration. We have transformed this spreadsheet into an ontology using highly-programmatic and pattern-driven ontology development. Critically, the spreadsheet remains part of the source for the ontology; the domain specialists are free to update it, and changes will percolate to the end ontology.
RESULTS: We have developed a new ontology describing immunological cell lines built by instantiating ontology design patterns written programmatically, using values from a spreadsheet catalogue.
CONCLUSIONS: This method employs a spreadsheet that was developed by domain experts. The spreadsheet is unconstrained in its usage and can be freely updated resulting in a new ontology. This provides a general methodology for ontology development using data generated by domain specialists.

PMID: 29179777 [PubMed – indexed for MEDLINE]

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