Cameron Buckner (Bochum) gives a talk at the MCMP Colloquium (26 April, 2012) titled "Dynamic Ontology". Abstract: A computational ontology is a formally-encoded specification of the concepts relevant to a subject domain (including their properties and relations holding between them) and a hierarchical classification of those concepts into categories and subcategories. Such representations can support a variety of tasks and tools which require semantic knowledge of domain resources. The standard method of ontology design makes use of “double experts” — users trained in both the target domain and computational ontology design — to manually produce maximally-correct domain descriptions. Unfortunately, double-experts are expensive and prone to bias, and when domains change or evolve, ontologies must be revised manually. While perhaps appropriate for large, deep-pocketed projects working on relatively stable domains such as the natural sciences, the approach is often not feasible for smaller, open-access projects working on more dynamic domains such as the humanities. To serve these projects, we have recommended an approach we call “dynamic ontology”. In dynamic ontology, more effort is placed on automating as much of the ontology design and evolution process as possible. Forgoing the use of double experts creates its own challenges, however; dynamic ontologists must be more creative in their methods of obtaining data for ontology construction and population, and problems of data inconsistency and validation loom large. In this talk, I will describe the how these challenges are addressed in the architecture of the Indiana Philosophy Ontology project, which uses a three-step process of statistical information retrieval, targeted solicitation of expert feedback, and machine reasoning to assemble a dynamic knowledge base for the discipline of philosophy.