Medical knowledge reengineering--converting major portions of the UMLS into a terminological knowledge base

Abstract

We describe a semi-automatic knowledge engineering approach for converting the human anatomy and pathology portion of the UMLS metathesaurus into a terminological knowledge base. Particular attention is paid to the proper representation of part-whole hierarchies, which complement taxonomic ones as a major hierarchy-forming principle for anatomical knowledge. Our approach consists of four steps. First, concept definitions are automatically generated from the metathesaurus, with LOOM as the target language. Second, integrity checking of the emerging taxonomic and partonomic hierarchies is automatically carried out by the terminological classifier. Third, terminological cycles and inconsistencies are manually eliminated and, in the last step, the knowledge base built this way is incrementally refined by a medical expert. Our experiments were run on a terminological knowledge base which is composed of 164,000 concepts and 76,000 relations. Empirical evidence for the lack of logical consistency, adequacy and improper granularity of the UMLS knowledge source is given, and finally, assessments of what kind of efforts are needed to render the formal target representation structures complete and empirically adequate.