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One of the challenges of ontological models is that to work, the data on which inferencing is to be done using the ontology must themselves have a meaning consistent with the ontology. In practical terms this means that the information model(s) of the data must be consistent with or mappable to the ontologies; it also means that the data themselves are likely to be tagged with terms from an ontology. For example, if the data record a 'allergy' for a patient, this must have the same meaning as 'allergy' does in the ontology. However, this is often not the case due to poorly defined terms; 'allergy' might have been used to mean 'an allergic reaction' or 'a diagnosed allergy. Ontologies can help here by allowing the detection of such ambiguities (see http://ontology.buffalo.edu/medo/Cologne.pdfandpdf) and by providing well-tested guidelines for how to deal with the corresponding distinctions
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