Abstract
We describe an ontology engineering methodology by which conceptual knowledge is extracted from an informal medical thesaurus (UMLS) and automatically converted into a formally sound description logics system. Our approach consists of four steps: concept definitions are automatically generated from the UMLS source, integrity checking of taxonomic and partonomic hierarchies is performed by the terminological classifier, cycles and inconsistencies are eliminated, and incremental refinement of the evolving knowledge base is performed by a domain expert. We report on knowledge engineering experiments with a terminological knowledge base composed of 164,000 concepts and 76,000 relations.
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Hahn, U., Schulz, S. (2000). Towards Very Large Terminological Knowledge Bases: A Case Study from Medicine. In: Hamilton, H.J. (eds) Advances in Artificial Intelligence. Canadian AI 2000. Lecture Notes in Computer Science(), vol 1822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45486-1_15
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DOI: https://doi.org/10.1007/3-540-45486-1_15
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