dc.date.accessioned2019-01-29T22:19:55Z
dc.date.accessioned2023-05-30T23:27:48Z
dc.date.available2019-01-29T22:19:55Z
dc.date.available2023-05-30T23:27:48Z
dc.date.created2019-01-29T22:19:55Z
dc.date.issued2013
dc.identifier16130073
dc.identifierhttp://repositorio.ucsp.edu.pe/handle/UCSP/15879
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6477691
dc.description.abstractLink prediction in a network is mostly based on information about the neighborhood topology of the nodes. Recently, the interest for hybrid link prediction approaches that combine topology information with information about the network individuals, has grown. However, considering the whole set of individuals may not be necessary and sometimes not even suitable. Therefore, mechanisms to automatically discover the relevant set of individuals are demanding. In this paper, we encompass this problem by proposing an algorithm that combines structure and semantic metrics to find the set of relevant individuals. We empirically evaluate this proposal analyzing the assertion role of these individuals when predicting a link through a probabilistic ontology.
dc.languageeng
dc.publisherCEUR-WS
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84922523322&partnerID=40&md5=817288f15cf814fe76247cadb0023209
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceRepositorio Institucional - UCSP
dc.sourceUniversidad Católica San Pablo
dc.sourceScopus
dc.subjectSemantics
dc.subjectTopology
dc.subjectLink prediction
dc.subjectNeighborhood topology
dc.subjectProbabilistic ontologies
dc.subjectSemantic metrics
dc.subjectTopology information
dc.subjectForecasting
dc.titleAssertion role in a hybrid link prediction approach through probabilistic ontology
dc.typeinfo:eu-repo/semantics/conferenceObject


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