DyKOSMap: A framework for mapping adaptation between biomedical knowledge organization systems.

Autor: Dos Reis JC; Institute of Computing, University of Campinas, Av. Albert Einstein, 1251, Cidade Universitária Zeferino Vaz, 13083-852 Campinas, SP, Brazil. Electronic address: julio.dosreis@ic.unicamp.br., Pruski C; Luxembourg Institute of Science and Technology, 29 Avenue John F. Kennedy, L-1855 Luxembourg, Luxembourg., Da Silveira M; Luxembourg Institute of Science and Technology, 29 Avenue John F. Kennedy, L-1855 Luxembourg, Luxembourg., Reynaud-Delaître C; Laboratoire de Recherche en Informatique, University of Paris-Sud, Bâtiment 650 (Ada Lovelace), 91405 Orsay, France.
Jazyk: angličtina
Zdroj: Journal of biomedical informatics [J Biomed Inform] 2015 Jun; Vol. 55, pp. 153-73. Date of Electronic Publication: 2015 Apr 15.
DOI: 10.1016/j.jbi.2015.04.001
Abstrakt: Background: Knowledge 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.
Methods: 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.
Results: 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.
Conclusions: 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.
(Copyright © 2015 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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