Bio-medical ontologies can contain a large number of concepts. Often many of these concepts are very similar to each other, and similar or identical to concepts found in other bio-medical databases. This presents both a challenge and opportunity: maintaining many similar concepts is tedious and fastidious work, which could be substantially reduced if the data could be derived from pre-existing knowledge sources. In this paper, we describe how we have achieved this for an ontology of the mitochondria using our novel ontology development environment, the Tawny-OWL library.
Ontologies allow complex descriptions of the world in a way that is both precise and computationally amenable — that is, computers can be used to check and query these descriptions. The mitochondria is a critical part of the cells of most organisms, being responsible for energy usage. We wished to build an ontology describing the current research on the mitochondria.
The more traditional approach to this, would have been to build the ontology from scratch; but many parts of the mitochondria, including the genes and proteins have already been described in other databases. Building from scratch on the basis of the data in these databases would be time-consuming, but also sensitive to change — if the database changes, our ontology would need updating too.
Instead we have used our new ontology development methodology to automatically extract this knowledge, and build the ontology for us providing what we describe as the scaffold for an ontology. In future, we will add more knowledge to this ontology, slowing building up the rich description of the mitochondrion that we are aiming for.