Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/74752
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dc.date.accessioned2021-04-26T11:00:10Z-
dc.date.available2021-04-26T11:00:10Z-
dc.date.issued2019-
dc.identifier.citationEllul, L. (2019). Read all about it!: a research support tool for automatically finding relevant prior and related work and sequencing it for reading (Bachelor's dissertation).en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/74752-
dc.descriptionB.SC.ICT(HONS)ARTIFICIAL INTELLIGENCEen_GB
dc.description.abstractThis dissertation describes a research tool that automatically finds prior work associated with a research paper. A paper is inputted into the tool and by iterating through the references, the tool builds a dependency graph of papers and determines a progression in which they should be read. This ensures that subjects introduced in later papers but explained in earlier papers are understood by the user. The PageRank algorithm, along with the Katz Distance, vector-space models and other algorithms will rank each papers’ importance. Less important papers can be omitted from the timeline and a visualization will indicate to the user which papers are more important than others. Since no Gold Standard is available, this project is a proof of concept. However, an evaluation was conducted using a Silver Standard, where multiple dependency graphs of virtual papers were used as testing mediums for this project. The aim of this project is to make studying easier and more fun for academics. The research tool uses Google Scholar as its primary digital library, therefore, it has the potential to help many academics studying a wide variety of subjects.en_GB
dc.language.isoenen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectData miningen_GB
dc.subjectWeb usage miningen_GB
dc.subjectInternet searchingen_GB
dc.titleRead all about it! : a research support tool for automatically finding relevant prior and related work and sequencing it for readingen_GB
dc.typebachelorThesisen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.publisher.institutionUniversity of Maltaen_GB
dc.publisher.departmentFaculty of Information and Communication Technology. Department of Artificial Intelligenceen_GB
dc.description.reviewedN/Aen_GB
dc.contributor.creatorEllul, Luke (2019)-
Appears in Collections:Dissertations - FacICT - 2019
Dissertations - FacICTAI - 2019

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