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dc.contributor.authorTrovati, Marcello
dc.contributor.authorBagdasar, Ovidiu
dc.date.accessioned2016-11-17T12:30:06Z
dc.date.available2016-11-17T12:30:06Z
dc.date.issued2014-03-26
dc.identifier.citationTrovati, M. and Bagdasar, O. (2014), 'Influence discovery in semantic networks: An initial approach', Proceedings of the 16th International Conference on Computer Modelling and Simulation (UKSim), Emmanuel College, Cambridge University, Cambridge: UK, 26-28 Marchen
dc.identifier.isbn9781479949229
dc.identifier.doi10.1109/UKSim.2014.48
dc.identifier.urihttp://hdl.handle.net/10545/620890
dc.description.abstractAssessing the influence between concepts, which include people, physical objects, as well as theoretical ideas, plays a crucial role in understanding and discovering knowledge. Despite the huge amount of literature on knowledge discovery in semantic networks, there has been little attempt to fully classify and investigate the influence, which also includes causality, of a semantic entity on another one as dynamical entities. In this paper we will introduce an approach to discover and assess influence among nodes in a semantic network, with the aim to provide a tool to identify its type and direction. Even though this is still being developed, the preliminary evaluation shows promising and interesting results.
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/7046055/en
dc.relation.urlhttp://uksim.info/uksim2014/uksim2014.htmen
dc.relation.urlhttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7045626en
dc.subjectSemanticsen
dc.subjectMathematical modelen
dc.subjectSocial networking sitesen
dc.subjectComputational modelingen
dc.titleInfluence discovery in semantic networks: An initial approachen
dc.typeMeetings and Proceedingsen
dc.contributor.departmentUniversity of Derbyen
dc.identifier.journalProceedings of the 16th International Conference on Computer Modelling and Simulation (UKSim)en
refterms.dateFOA2019-02-28T15:00:04Z
html.description.abstractAssessing the influence between concepts, which include people, physical objects, as well as theoretical ideas, plays a crucial role in understanding and discovering knowledge. Despite the huge amount of literature on knowledge discovery in semantic networks, there has been little attempt to fully classify and investigate the influence, which also includes causality, of a semantic entity on another one as dynamical entities. In this paper we will introduce an approach to discover and assess influence among nodes in a semantic network, with the aim to provide a tool to identify its type and direction. Even though this is still being developed, the preliminary evaluation shows promising and interesting results.


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