Library and Information Studies

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Browsing Library and Information Studies by Author "Chipangila, Bertha"

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    Improved discoverability of digital objects in institutional repositories using controlled vocabularies.
    (IEEE, 2021-09-27) Chipangila, Bertha; Liswaniso, Eric; Mawila, Andrew; Mwanza, Philomena; Nawila, Daisy; M’sendo, Robert; Nyirenda, Mayumbo; Phiri, Lighton
    Higher Education Institutions (HEIs) utilise Insti- tutional Repositories (IRs) to electronically store and make available scholarly research output produced by faculty staff and students. With the continued increase of scholarly research output produced, accurate and comprehensive association of subject headings to digital objects, during ingestion into IRs is crucial for effective discoverability of the objects and, additionally facilitating the discovery of related content. This paper outlines a case study conducted at an HEI—The University of Zambia—in order to demonstrate the effectiveness of integrating controlled subject vocabularies during the ingestion of digital objects in to IRs. A situational analysis was conducted to understand how subject headings are associated with digital objects and to analyse subject headings associated with already ingested digital objects. In addition, an exploratory study was conducted to determine domain-specific subject headings to be integrated with the IR. Furthermore, a usability study was conducted in order to comparatively determine the usefulness of using controlled vocabularies during the ingestion of digital objects into IRs. Finally, multi-label classification experiments were carried out where digital objects were assigned with more than one class. The results of the study revealed that the majority of digital objects are currently associated with two or less subject headings (71.2 %), with a significant number of subject headings (92.1 %) being associated with a single publication. The comparative study suggests that IRs integrated with controlled vocabularies are perceived to be more usable (SUS Score = 68.9 ) when compared with IRs without controlled vocabularies (SUS Score = 66.2 ). The effectiveness of the multi-label arXiv subjects classifier demonstrates the viability of integrating automated techniques for subject classification.