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

Abstract：

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.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Dos Reis JC,Pruski C,Da Silveira M,Reynaud-Delaître C

doi

10.1016/j.jbi.2015.04.001

subject

Has Abstract

pub_date

2015-06-01 00:00:00

pages

153-73

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(15)00068-4

journal_volume

55

pub_type

杂志文章

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