Discovery of ontologies from knowledge bases

Current approaches to building knowledge-based systems propose the development of an ontology as a precursor to building the problem-solver. This paper outlines an attempt to do the reverse and discover interesting ontologies from systems built without the ontology being explicit. In particular the paper considers large classification knowledge bases used for the interpretation of medical chemical pathology results and built using Ripple-Down Rules (RDR). The rule conclusions in these knowledge bases provide free-text interpretations of the results rather than explicit classes. The goal is to discover implicit ontological relationships between these interpretations as the system evolves. RDR allows for incremental development and the goal is that the ontology emerges as the system evolves. The results suggest that approach has potential, but further investigation is required before strong claims can be made.