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dc.contributor.authorde Mel, G.
dc.contributor.authorŞensoy, Murat
dc.contributor.authorVasconcelos, W.
dc.contributor.authorNorman, T. J.
dc.date.accessioned2014-07-08T10:38:22Z
dc.date.available2014-07-08T10:38:22Z
dc.date.issued2013-02
dc.identifier.issn0952-1976
dc.identifier.urihttp://hdl.handle.net/10679/466
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0952197612003119#
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.en_US
dc.description.abstractIn this paper, we present Ontological Logic Programming (OLP), a novel approach that combines logic programming with ontological reasoning. OLP enables the use of ontological terms (i.e., individuals, classes and properties) directly within logic programmes. The interpretation of these terms is delegated to an ontology reasoner during the interpretation of the programme. Unlike similar approaches, OLP makes use of the full capacity of both ontological reasoning and logic programming. We evaluate the computational properties of OLP in different settings and show that its performance can be significantly improved using caching mechanisms. We then introduce a comprehensive sensor-task selection solution based on OLP and discuss the benefits one can obtain by using OLP. The solution is based on a set of interlinking ontologies that capture the crucial domain knowledge of sensor networks. We then make use of OLP to create and manage complex concepts in the domain as well as to implement effective resource-task assignment algorithms, which compute appropriate resources for tasks such that they sufficiently cover the tasks needs. We compare the advantages of OLP with a knowledge-based set-covering mechanism for resource-task selection.en_US
dc.description.sponsorshipU.S. Army Research Laboratory ; U.K. Ministry of Defence
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofEngineering Applications of Artificial Intelligence
dc.rightsrestrictedAccess
dc.titleA hybrid reasoning mechanism for effective sensor selection for tasksen_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID41438
dc.contributor.ozuauthorŞensoy, Murat
dc.identifier.volume26
dc.identifier.issue2
dc.identifier.startpage873
dc.identifier.endpage887
dc.identifier.wosWOS:000314672400018
dc.identifier.doi10.1016/j.engappai.2012.12.003
dc.subject.keywordsLogic programmingen_US
dc.subject.keywordsSemantic weben_US
dc.subject.keywordsKnowledge-based resource selectionen_US
dc.identifier.scopusSCOPUS:2-s2.0-84872685024
dc.contributor.authorMale1


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