An automated approach to mapping external terminologies to the UMLS

Nowadays, providing interoperability between different biomedical terminologies is a critical issue for efficient information sharing. One problem making interoperability difficult is the lack of automated methods simplifying the mapping process. In this study, we propose an automated approach to mapping external terminologies to the Unified Medical Language System (UMLS). Our approach applies a sequential combination of two basic matching methods classically used in ontology matching. First, a lexical technique identifies similar strings between the external terminology and the UMLS. Second, a structure-based technique validates, in part, the lexical alignment by computing paths to top-level concepts and checking the compatibility of these top-level concepts across the external terminology and the UMLS. The method was applied to the mapping of the large-scale biomedical thesaurus EMTREE to the complete UMLS Metathesaurus. In total, 47.9% coverage of EMTREE terms was reached, leading to 80% coverage of EMTREE concepts. Our method has revealed a high compatibility in 6 out of 15 top-level categories across terminologies. The validation of lexical mappings ranges over 75.8% of the total lexical alignment. Overall, the method rules out a total of 6927 (7.9%) lexical mappings, with a global precision of 78%.