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dc.contributor.authorMartínez-Romero, Marcos
dc.contributor.authorVázquez-Naya, José
dc.contributor.authorNóvoa, Francisco
dc.contributor.authorVázquez, Guillermo
dc.contributor.authorPereira-Loureiro, Javier
dc.date.accessioned2018-06-01T08:01:33Z
dc.date.available2018-06-01T08:01:33Z
dc.date.issued2013
dc.identifier.citationMartínez-Romero M, Vázquez-Naya JM, Nóvoa FJ, Vázquez G, Pereira J. A genetic algorithms-based approach for optimizing similarity aggregation in ontology matching. Ponencia presentada en International Work-Conference on Artificial Neural Networks. IWANN 2013; 2013 Jun 12-14; Puerto de la Cruz, Tenerife. Berlin: Springer; 2013. p.435-444 (Lecture Notes in Computer Science; 7902)es_ES
dc.identifier.isbn978-3-642-38678-7
dc.identifier.isbn978-3-642-38679-4
dc.identifier.urihttp://hdl.handle.net/2183/20776
dc.description.abstract[Abstract] Ontology matching consists of finding the semantic relations between different ontologies and is widely recognized as an essential process to achieve an adequate interoperability between people, systems or organizations that use different, overlapping ontologies to represent the same knowledge. There are several techniques to measure the semantic similarity of elements from separate ontologies, which must be adequately combined in order to obtain precise and complete results. Nevertheless, combining multiple similarity measures into a single metric is a complex problem, which has been traditionally solved using weights determined manually by an expert, or through general methods that do not provide optimal results. In this paper, a genetic algorithms based approach to aggregate different similarity metrics into a single function is presented. Starting from an initial population of individuals, each one representing a combination of similarity measures, our approach allows to find the combination that provides the optimal matching quality.es_ES
dc.description.sponsorshipInstituto de Salud Carlos III; FISPI10/02180es_ES
dc.description.sponsorshipPrograma Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT0366es_ES
dc.description.sponsorshipXunta de Galicia; CN2012/217es_ES
dc.description.sponsorshipXunta de Galicia; CN2011/034es_ES
dc.description.sponsorshipXunta de Galicia; CN2012/211es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.urihttps://doi.org/10.1007/978-3-642-38679-4_43es_ES
dc.rightsThe final publication is avaliable at Springer Linkes_ES
dc.subjectGenetic algorithmses_ES
dc.subjectOntology matchinges_ES
dc.subjectOntologieses_ES
dc.subjectSemantic webes_ES
dc.titleA genetic algorithms-based approach for optimizing similarity aggregation in ontology matchinges_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleLecture Notes in Computer Sciencees_ES
UDC.volume7902es_ES
UDC.startPage435es_ES
UDC.endPage444es_ES
UDC.conferenceTitleInternational Work-Conferernce on Artificial Neural Networkses_ES


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