Optimizing Ontology Alignments by Using Neural NSGA-II

Inthisarticle,theauthorsproposeanewhybridapproachbasedonacontinuousNon-dominated SortingGeneticAlgorithmII(NSGA-II)andaneuralnetworktorefinethealignmentresults.This approachconsistsofthreephases:(i)pre-alignmentphasewhichallowstoidentifytheformatsof inputontologies,toadaptthemandtotransformthemintoOntologyWebLanguage(OWL)inorder tosolvetheproblemofheterogeneityofrepresentation.(ii)alignmentphasewhichcombinessyntactic andlinguisticmatchingtechniquesandmethods,basedontherelevantattributesperdifferentpoints ofsyntacticandstructuraltechnic.(iii)Thepost-alignmentphasewhichoptimizesthematchingby ahybridtechniqueofcontinuousNSGA-IIandnetworksofneurons.Thisapproachiscompared withthegreatestsystemspertheOntologyAlignmentEvaluationInitiative(OAEI)standard.The experimentalresultsappearthattheproposedapproachiseffective. KEywORdS Matching, Neural NSGA-II, NSGA-II, Ontology Alignment, Semantic Web

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