Show simple item record

dc.contributor.advisorO'Sullivan, Declan
dc.contributor.authorTai, Wei
dc.date.accessioned2016-11-07T16:30:18Z
dc.date.available2016-11-07T16:30:18Z
dc.date.issued2011
dc.identifier.citationWei Tai, 'Automatic reasoner composition and selection', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2011, pp 284
dc.identifier.otherTHESIS 9727
dc.identifier.urihttp://hdl.handle.net/2262/77668
dc.description.abstractThe development of OWL and OWL reasoning technologies has enabled them to be used for knowledge base (KB) modelling and/or intelligent data processing in applications of various areas. However the computation and memory intensive nature of OWL reasoners impedes the deployment of OWL ontology reasoning on resource-constrained devices. In order to address this issue, a possible approach is to compose the reasoners according to their application characteristics such that unnecessary reasoning capabilities are not loaded. This thesis introduces two novel automatic reasoner composition approaches, a selective rule loading algorithm and a two-phase RETE algorithm, that compose rule-entailment reasoners both at the rule level and inside the reasoning algorithm based on the ontology expressivity, in order to reduce resource consumption for reasoning on resource-constrained devices. With the growth of usage of ontology reasoners and the introduction of new reasoner characteristics, it is envisaged that reasoner selection in the future will become too complicated for the current consultation based process between application developers and reasoner experts. In addition the thesis proposes a semi-automatic reasoner selection process (RESP) that allows users to independently select a most appropriate reasoner for their applications according to application characteristics. The solutions to the problems of how to achieve resource constrained reasoning and more automatic reasoning selection, have been respectively implemented, in a resource-constrained composable reasoner (COROR) and a semi-automatic reasoner selection tool (TARS). Evaluation of the solutions indicates that the designed reasoner composition algorithms greatly reduce the time and memory requirement for reasoning and that proposed reasoner selection process helps users independently select a most appropriate reasoner for their applications, both of which will contribute to advancing the state of the art in the usage of reasoning within semantic applications.
dc.format1 volume
dc.language.isoen
dc.publisherTrinity College (Dublin, Ireland). School of Computer Science & Statistics
dc.relation.isversionofhttp://stella.catalogue.tcd.ie/iii/encore/record/C__Rb15152913
dc.subjectComputer Science, Ph.D.
dc.subjectPh.D. Trinity College Dublin
dc.titleAutomatic reasoner composition and selection
dc.typethesis
dc.type.supercollectionrefereed_publications
dc.type.supercollectionthesis_dissertations
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (Ph.D.)
dc.rights.ecaccessrightsopenAccess
dc.format.extentpaginationpp 284
dc.description.noteTARA (Trinity's Access to Research Archive) has a robust takedown policy. Please contact us if you have any concerns: rssadmin@tcd.ie


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record