In this extended abstract we examine the principles that underlie the construction of what we call Ontologies in Variation — a human-comprehensible knowledge representation scheme for natural kinds, objects, and concepts that captures both prototypical (or canonical) properties of classes of objects as well as those properties that are in variation. A fundamental characteristic of our work is that the variability captured in our representation is derived from data and as such that the provenance of statistical knowledge — the dataset — is directly associated with the ontology. This reliance on empirical data directs us towards a frequentist view of variation as statistical assertions, in contrast to much of the current work that integrates logic and uncertainty. Our formalism's novelty lies in the strategic complementation of axiomatic knowledge by statistical knowledge, and by the desire to preserve human comprehension of the resulting representation. We illustrate this work in the context of an ongoing project to create a representation of human anatomy — a queryable digital anatomy book that fits all of us in some variation.

We present an ontology consisting of a theory of spatial dimension and a theory of dimension-independent mereological and topological relations in space. Though both are fairly weak axiomatizations, their interplay suffices to define various mereotopological relations and to make any necessary dimension constraints explicit. We show that models of the INCH Calculus and the Region-Connection Calculus (RCC) can be obtained from extensions of the proposed ontology.