title: 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.
authors.affiliation: 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.
authors.forename: Julio Cesar
authors.initials: JC
authors.lastname: Dos Reis
authors.affiliation: Luxembourg Institute of Science and Technology, 29 Avenue John F. Kennedy, L-1855 Luxembourg, Luxembourg.
authors.forename: Cédric
authors.initials: C
authors.lastname: Pruski
authors.affiliation: Luxembourg Institute of Science and Technology, 29 Avenue John F. Kennedy, L-1855 Luxembourg, Luxembourg.
authors.forename: Marcos
authors.initials: M
authors.lastname: Da Silveira
authors.affiliation: Laboratoire de Recherche en Informatique, University of Paris-Sud, Bâtiment 650 (Ada Lovelace), 91405 Orsay, France.
authors.forename: Chantal
authors.initials: C
authors.lastname: Reynaud-Delaître
mesh_terms: D000068598:Data Accuracy; D057225:Data Mining / Q000379:methods*; D003628:Database Management Systems / Q000458:organization & administration*; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D051188:Knowledge Bases; D008498:Medical Record Linkage / Q000379:methods*; D009323:Natural Language Processing; D012660:Semantics; D012984:Software
keywords: Biomedical ontologies; Mapping adaptation; Mapping evolution; Mapping maintenance; Ontology alignment; Ontology evolution