240,000 concepts and relations-towards mega knowledge bases for real-world applications

We describe an ontology engineering methodology by which conceptual knowledge is extracted from an informal medical thesaurus (UMLS) and automatically converted into a formal description logics system (LOOM). Our approach consists of four steps: concept definitions are automatically generated from the UMLS, Integrity checking of taxonomic and partonomic hierarchies is performed by LOOM´s terminological classifier, cycles and inconsistencies are eliminated, as well as incremental refinement of the evolving knowledge base is performed by a domain expert. We report an experiments with a very large knowledge base composed of 164,000 concepts and 76,000 relations.