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dc.contributor.advisorNand, Parma
dc.contributor.authorIslam, Md Rashedul
dc.date.accessioned2018-04-08T21:29:16Z
dc.date.available2018-04-08T21:29:16Z
dc.date.copyright2018
dc.identifier.urihttp://hdl.handle.net/10292/11499
dc.description.abstractThis thesis aims to design and implement an information system which will mine information from online annual reports across organizations in New Zealand which have made efforts towards enhancing Matauranga Māori in line with the New Zealand government's long term strategic objectives. A system has been proposed that will be able to traverse through a large number of annual reports and extract information on the presence and the extent activities related to Matauranga across institutions. The extracted information includes examples of initiatives towards education, health, and housing, as well as data on the success rate of such initiatives. A total of 216 annual reports published by 48 different organizations in the period 2008-2015 were used as the data source and they include governmental, non-governmental, private and trust organizations. The proposed system makes use of NLP, the Semantic web, Ontology and RDF technology to extract, encode and present the information. Four sets of relations have been developed for four different sectors which include Health, Education, Finance, and Language and Culture. It resulted in the identification of 330 triples (subject-predicate-object) which encodes pertinent information in the organization concerning Māori and Pacific people. A tool has been developed and implemented for converting normal text into ontologies to analyze them. In order to do this, we used open NLP derived from Apache, Protégé from Stanford University, Owl GRED and the Visual Web Data was used. The ontologies developed were analyzed using XML and graphical analysis which shows how natural text can be converted into relational ontologies with Resource Description Framework presentation.en_NZ
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.subjectNLPen_NZ
dc.subjectSemantic weben_NZ
dc.subjectOntologyen_NZ
dc.subjectRDFen_NZ
dc.subjectAnnual reportsen_NZ
dc.subjectMāori affairsen_NZ
dc.titleInformation Mining from New Zealand published annual reports relating Māori affairsen_NZ
dc.typeThesisen_NZ
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelMasters Theses
thesis.degree.nameMaster of Computer and Information Sciencesen_NZ
dc.rights.accessrightsOpenAccess
dc.date.updated2018-03-20T07:45:35Z


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