In the design, implementation, and long-term deployment of computational ontologies, knowledge modelers face several enduring challenges. For one, computational ontologies have often been designed without sufficient logical rigor, which may come with pragmatic costs in terms of the expressive power, clarity, and interoperability of the scheme (Guarino 1995; Smith 2006; Arp, this volume). The economics of ontology design is also a problem, generally requiring significant time from scholars specially-trained in both the target domain and the principles and methods of computational ontology design (hereafter, “double experts”). Obsolescence looms large, as change in the problem domain or our understanding of it can render all that design effort useless, in the best case requiring more time from double experts to manually evolve the ontology (Flouris et al. 2006; Ceusters and Smith 2006) and in the worst cases taking a project back to the drawing board.