Towards a broad-coverage biomedical ontology based on description logics.
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 on experiments with a very large knowledge base composed of 164,000 concepts and 76,000 relations.
Hahn
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LIF Text Knowledge Engineering Lab, Freiburg University, D-79085 Freiburg, Germany.
Schulz
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D001185:Artificial Intelligence; D001698:Biomedical Engineering; D002965:Classification; D019295:Computational Biology; D016015:Logistic Models; D017432:Unified Medical Language System