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    Automatic classification of digital objects for improved metadata quality of electronic theses and dissertations in institutional repositories.

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    Date
    2020
    Author
    Phiri, Lighton
    Type
    Article
    Language
    en
    Metadata
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    Abstract
    Higher education institutions typically employ Institutional Repositories (IRs) in order to curate and make available Electronic Theses and Dissertations (ETDs). While most of these IRs are implemented with self-archiving functionalities, self-archiving practices are still a challenge. This arguably leads to inconsistencies in the tagging of digital objects with descriptive metadata, potentially compromising searching and browsing of scholarly research output in IRs. This paper proposes an approach to automatically classify ETDs in IRs, using supervised machine learning techniques, by extracting features from the minimum possible input expected from document authors: the ETD manuscript. The experiment results demonstrate the feasibility of automatically classifying IR ETDs and, additionally, ensuring that repository digital objects are appropriately structured. Automatic classification of repository objects has the obvious benefit of improving the searching and browsing of content in IRs and further presents opportunities for the implementation of third-party tools and extensions that could potentially result in effective self-archiving strategies.
    URI
    https://dx.doi.org/10.1504/IJMSO.2020.112804
    http://dspace.unza.zm/handle/123456789/6969
    Citation
    Phiri, L. (2020). Automatic Classification of Digital Objects for Improved Metadata Quality of Electronic Theses and Dissertations in Institutional Repositories. International Journal of Metadata, Semantics and Ontologies, 14(3), 234–248. https://doi.org/10.1504/IJMSO.2020.112804
    Publisher
    International Journal of Metadata, Semantics and Ontologies (IJMSO)
    Subject
    Digital Libraries
    Dublin Core
    OAI-PMH
    Document Classification
    Automatic Classification
    Digital Objects
    Metadata Quality
    Electronic Theses and Dissertations
    Institutional Repositories
    Self-Archiving
    Collections
    • Library and Information Studies [78]

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