Towards Natural Language Question Generation For The Validation Of Ontologies And Mappings

dc.creatorBen Abacha
dc.creatorAsma; Dos Reis
dc.creatorJulio Cesar; Mrabet
dc.creatorYassine; Pruski
dc.creatorCedric; Da Silveira
dc.creatorMarcos
dc.date2016
dc.dateagos
dc.date2017-11-13T13:43:55Z
dc.date2017-11-13T13:43:55Z
dc.date.accessioned2018-03-29T05:58:38Z
dc.date.available2018-03-29T05:58:38Z
dc.identifierJournal Of Biomedical Semantics. Biomed Central Ltd, v. 7, p. , 2016.
dc.identifier2041-1480
dc.identifierWOS:000381668900001
dc.identifier10.1186/s13326-016-0089-6
dc.identifierhttps://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0089-6
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/328665
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1365690
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionThe increasing number of open-access ontologies and their key role in several applications such as decision-support systems highlight the importance of their validation. Human expertise is crucial for the validation of ontologies from a domain point-of-view. However, the growing number of ontologies and their fast evolution over time make manual validation challenging. Methods: We propose a novel semi-automatic approach based on the generation of natural language (NL) questions to support the validation of ontologies and their evolution. The proposed approach includes the automatic generation, factorization and ordering of NL questions from medical ontologies. The final validation and correction is performed by submitting these questions to domain experts and automatically analyzing their feedback. We also propose a second approach for the validation of mappings impacted by ontology changes. The method exploits the context of the changes to propose correction alternatives presented as Multiple Choice Questions. Results: This research provides a question optimization strategy to maximize the validation of ontology entities with a reduced number of questions. We evaluate our approach for the validation of three medical ontologies. We also evaluate the feasibility and efficiency of our mappings validation approach in the context of ontology evolution. These experiments are performed with different versions of SNOMED-CT and ICD9. Conclusions: The obtained experimental results suggest the feasibility and adequacy of our approach to support the validation of interconnected and evolving ontologies. Results also suggest that taking into account RDFS and OWL entailment helps reducing the number of questions and validation time. The application of our approach to validate mapping evolution also shows the difficulty of adapting mapping evolution over time and highlights the importance of semi-automatic validation.
dc.description7
dc.descriptionSao Paulo Research Foundation (FAPESP) [2014/14890-0]
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.languageEnglish
dc.publisherBiomed Central Ltd
dc.publisherLondon
dc.relationJournal of Biomedical Semantics
dc.rightsaberto
dc.sourceWOS
dc.subjectOntology Validation
dc.subjectMapping Validation
dc.subjectQuestion Generation
dc.subjectKnowledge Management
dc.titleTowards Natural Language Question Generation For The Validation Of Ontologies And Mappings
dc.typeArtículos de revistas


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