Busca avançada
Ano de início
Entree
(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

Texto completo
Autor(es):
Dos Reis, Julio Cesar [1] ; Pruski, Cedric [2] ; Da Silveira, Marcos [2] ; Reynaud-Delaitre, Chantal [3]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP - Brazil
[2] Luxembourg Inst Sci & Technol, L-1855 Luxembourg - Luxembourg
[3] Univ Paris 11, Lab Rech Informat, F-91405 Orsay - France
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF BIOMEDICAL INFORMATICS; v. 55, p. 153-173, JUN 2015.
Citações Web of Science: 3
Resumo

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. (C) 2015 Elsevier Inc. All rights reserved. (AU)

Processo FAPESP: 14/14890-0 - IMEanT: métodos para considerar significados e intenções em sistemas colaborativos interativos
Beneficiário:Julio Cesar dos Reis
Linha de fomento: Bolsas no Brasil - Pós-Doutorado