While genomic and proteomic information describe the over-all cellular machinery available to an organism, the metabolic profile of an individual at a given time provides a canvas as to the current phys-iological state. Concentration levels of relevant metabolites vary under different conditions, in particular, in the presence or absence of different disorders. Metabolite concentrations thus mediate an important link be-tween chemistry and biology, contributing to a systems-wide understand-ing of biological processes and pathways. However, there are a number of challenges in the ontological representation of such information. Firstly, concentration information is numeric and ranges over continu-ous values, while ontologies consist of discrete classes. Secondly, ontolo-gies usually model only what is certain, and their logical formalisms are adapted to reasoning from certain axioms to logical deductions, how-ever, the link between chemicals and diseases via concentration levels, like many threshold phenomena, is both uncertain and vague. In this paper we evaluate the representation of this knowledge using a combination of concrete domains and probabilistic reasoning. We parse concentration values from HMDB and create an ontology able to dis-tinguish normal from abnormal concentrations and able to evaluate a probabilistic risk category for the presence of an associated disorder.