Dykosmap: A Framework For Mapping Adaptation Between Biomedical Knowledge Organization Systems.

dc.creatorDos Reis, Julio Cesar
dc.creatorPruski, Cédric
dc.creatorDa Silveira, Marcos
dc.creatorReynaud-Delaître, Chantal
dc.date2015-Jun
dc.date2016-05-23T19:43:09Z
dc.date2016-05-23T19:43:09Z
dc.date.accessioned2018-03-29T01:30:20Z
dc.date.available2018-03-29T01:30:20Z
dc.identifierJournal Of Biomedical Informatics. v. 55, p. 153-173, 2015-Jun.
dc.identifier1532-0480
dc.identifier10.1016/j.jbi.2015.04.001
dc.identifierhttp://www.ncbi.nlm.nih.gov/pubmed/25889690
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/235905
dc.identifier25889690
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1304148
dc.descriptionKnowledge Organization Systems (KOS) and their associated mappings play a central role in several decision support systems. However, by virtue of knowledge evolution, KOS entities are modified over time, impacting mappings and potentially turning them invalid. This requires semi-automatic methods to maintain such semantic correspondences up-to-date at KOS evolution time. We define a complete and original framework based on formal heuristics that drives the adaptation of KOS mappings. Our approach takes into account the definition of established mappings, the evolution of KOS and the possible changes that can be applied to mappings. This study experimentally evaluates the proposed heuristics and the entire framework on realistic case studies borrowed from the biomedical domain, using official mappings between several biomedical KOSs. We demonstrate the overall performance of the approach over biomedical datasets of different characteristics and sizes. Our findings reveal the effectiveness in terms of precision, recall and F-measure of the suggested heuristics and methods defining the framework to adapt mappings affected by KOS evolution. The obtained results contribute and improve the quality of mappings over time. The proposed framework can adapt mappings largely automatically, facilitating thus the maintenance task. The implemented algorithms and tools support and minimize the work of users in charge of KOS mapping maintenance.
dc.description55
dc.description153-173
dc.languageeng
dc.relationJournal Of Biomedical Informatics
dc.relationJ Biomed Inform
dc.rightsembargo
dc.sourcePubMed
dc.subjectBiomedical Ontologies
dc.subjectMapping Adaptation
dc.subjectMapping Evolution
dc.subjectMapping Maintenance
dc.subjectOntology Alignment
dc.subjectOntology Evolution
dc.titleDykosmap: A Framework For Mapping Adaptation Between Biomedical Knowledge Organization Systems.
dc.typeArtículos de revistas


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