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1、PR-OWL:AFrameworkforProbabilisticOntologiesPauloC.G.COSTA,KathrynB.LASKEYTheVolgenauSchoolofInformationTechnologyandEngineeringGeorgeMasonUniversity4444UniversityDriveFairfax,VA22032-4444USA[pcosta,klaskey]@gmu.eduAbstract.Acrossawiderangeofdomains,thereisanurgentn
2、eedforawell-foundedapproachtoincorporatinguncertainandincompleteknowledgeintofor-maldomainontologies.Althoughthissubjectisreceivingincreasingattentionfromontologyresearchers,thereisasyetnobroadconsensusonthedefinitionofaprobabilisticontologyandonthemostsuitableappr
3、oachtoextendingcurrenton-tologylanguagestosupportuncertainty.Thispaperpresentstwocontributionstodevelopingacoherentframeworkforprobabilisticontologies:(1)aformaldefini-tionofaprobabilisticontology,and(2)anextensionoftheOWLWebOntologyLanguagethatisconsistentwithourf
4、ormaldefinition.Thisextension,PR-OWL,isbasedonMulti-EntityBayesianNetworks(MEBN),afirst-orderBayesianlogicthatunifiesBayesianprobabilitywithFirst-OrderLogic.Assuch,PR-OWLcom-binesthefullrepresentationpowerofOWLwiththeflexibilityandinferentialpowerofBayesianlogic.Ke
5、ywords.ProbabilisticOntologies,PR-OWL,MEBN,Bayesiannetworks,un-certainty,SemanticWeb,knowledgesharingIntroductionSinceitsadoptioninthefieldofInformationSystems,thetermontologyhasbeengivenmanydifferentdefinitions.Acommonunderlyingassumptionisthatclassicallogicwouldp
6、rovidetheformalfoundationforknowledgerepresentationandreasoning.Untilrecently,theoryandmethodsforrepresentingandreasoningwithuncertainandincompleteknowledgehavebeenneglectedalmostentirely.However,asresearchonknowledgeengineeringandapplicationsofontologiesmatures,th
7、eubiquityandimpor-tanceofuncertaintyacrossawidearrayofapplicationareashasgeneratedconsumerdemandforontologyformalismsthatcancaptureuncertainty.Althoughinterestinprobabilisticontologieshasbeengrowing,thereisasyetnocommonlyacceptedformaldefinitionoftheterm.Wedemonstr
8、atethataugmentinganontologytocarrynumericaland/orstructuralinformationaboutprobabilisticrelation-shipsisnotenoughtodeemitaprobabilisticontology.T
