Auditing the Unified Medical Language System with Semantic Methods

Abstract Objective: The National Library of Medicine's (NLM) Unified Medical Language System (UMLS) includes a Metathesaurus (Meta), which is a compilation of medical terms drawn from over 30 controlled vocabularies, and a Semantic Net, which contains the semantic types used to categorize Meta concepts and the semantic relations to connect them. Meta has been constructed through lexical matching techniques and human review. The purpose of this study was to audit the Meta using semantic techniques to identify possible inconsistencies. Methods: Five different techniques were applied: (1) detection of ambiguity in Meta concepts with two or more semantic types, (2) detection of interchangeable keyword synonyms, (3) detection of redundant pairs of Meta concepts (using lexical matching combined with keyword synonyms), (4) detection of inconsistent parent-child relationships in Meta (based on the semantic type information), and (5) discovery of pairs of semantic types for which relations could be added to the Semantic Net, based on “other” relationships between Meta concepts. Results: Of 57,592 concepts with multiple semantic types, 1817 (3.2%) were judged to be ambiguous. Keyword analysis showed 7121 pairs of interchangeable words. Using the keyword pairs, 5031 pairs of potentially redundant concepts were suggested, of which 3274 (65.1%) were judged to actually be redundant. Review of the 100,586 parent-child relationships revealed 544 (0.54%) that were incorrect. Review of the 219,664 “Other” relationships suggested 1299 places in the Semantic Net where relations between pairs of semantic types could be added. Conclusion: Semantic techniques, alone or in combination, can be used to audit the UMLS to detect inconsistencies that are not detectable through lexical techniques alone. Use of these methods to augment the UMLS maintenance process will lead to improvement in the UMLS.