title: Mapping biomedical vocabularies: a semi-automated term matching approach.
abstract: Biomedical vocabularies vary in scope, and it is often necessary to utilize multiple vocabularies simultaneously in order to cover the full range of concepts relevant to a given biomedical application. However, as the number and size of these resources grow both redundancy (i.e., different vocabularies containing similar terms) and inconsistency (i.e., different terms in multiple vocabularies referring to the same entity) between the vocabularies increase. Therefore, there is a need for automatically aligning vocabularies. In this paper, we explore and propose new methods for detecting probable matches between two vocabularies. The methods build upon existing string similarity functions, enhancing these functions for the context of semi-automated vocabulary matching.
authors.affiliation: Health & Biomedical Informatics Centre, The University of Melbourne, Australia.
authors.forename: Bahadorreza
authors.initials: B
authors.lastname: Ofoghi
authors.affiliation: Health & Biomedical Informatics Centre, The University of Melbourne, Australia.
authors.forename: Guillermo H
authors.initials: GH
authors.lastname: Lopez-Campos
authors.affiliation: Health & Biomedical Informatics Centre, The University of Melbourne, Australia.
authors.forename: Fernando Jose
authors.initials: FJ
authors.lastname: Martin Sanchez
authors.affiliation: Dept. of Computing and Information Systems, The University of Melbourne, Australia.
authors.forename: Karin
authors.initials: K
authors.lastname: Verspoor
mesh_terms: D064229:Biological Ontologies; D000069550:Machine Learning; D008498:Medical Record Linkage / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D009626:Terminology as Topic