Controlled vocabularies in digital libraries: challenges and solutions for increased discoverability of digital objects
Digital Library Systems are widely used in the Higher Education sector, through the use of Institutional Repositories (IRs), to collect, store, manage and make available scholarly research output produced by Higher Education Institutions (HEIs). This wide application of IRs is a direct response to t...
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Published in | International journal on digital libraries Vol. 25; no. 2; pp. 139 - 155 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2024
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
ISSN | 1432-5012 1432-1300 |
DOI | 10.1007/s00799-023-00374-1 |
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Abstract | Digital Library Systems are widely used in the Higher Education sector, through the use of Institutional Repositories (IRs), to collect, store, manage and make available scholarly research output produced by Higher Education Institutions (HEIs). This wide application of IRs is a direct response to the increase in scholarly research output produced. In order to facilitate discoverability of digital content in IRs, accurate, consistent and comprehensive association of descriptive metadata to digital objects during ingestion into IRs is crucial. However, due to human errors resulting from complex IR ingestion workflows, most digital content in IRs have incorrect and inconsistent descriptive metadata. While there exists a broad spectrum of descriptive metadata elements, subject headings present a classic example of a crucial metadata element that adversely affects discoverability of digital content when incorrectly and inconsistently specified. 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 a noticeable number of digital content is associated with incorrect subject categories and, additionally, associated with few subjects headings: two or less subject headings (71.2
%
), with a significant number of subject headings (92.1
%
) being associated with a single publication. A comparative study conducted 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). Furthermore, the effectiveness of the multi-label arXiv subjects classifier demonstrates the viability of integrating automated techniques for subject classification. |
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AbstractList | Digital Library Systems are widely used in the Higher Education sector, through the use of Institutional Repositories (IRs), to collect, store, manage and make available scholarly research output produced by Higher Education Institutions (HEIs). This wide application of IRs is a direct response to the increase in scholarly research output produced. In order to facilitate discoverability of digital content in IRs, accurate, consistent and comprehensive association of descriptive metadata to digital objects during ingestion into IRs is crucial. However, due to human errors resulting from complex IR ingestion workflows, most digital content in IRs have incorrect and inconsistent descriptive metadata. While there exists a broad spectrum of descriptive metadata elements, subject headings present a classic example of a crucial metadata element that adversely affects discoverability of digital content when incorrectly and inconsistently specified. 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 a noticeable number of digital content is associated with incorrect subject categories and, additionally, associated with few subjects headings: two or less subject headings (71.2
%
), with a significant number of subject headings (92.1
%
) being associated with a single publication. A comparative study conducted 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). Furthermore, the effectiveness of the multi-label arXiv subjects classifier demonstrates the viability of integrating automated techniques for subject classification. Digital Library Systems are widely used in the Higher Education sector, through the use of Institutional Repositories (IRs), to collect, store, manage and make available scholarly research output produced by Higher Education Institutions (HEIs). This wide application of IRs is a direct response to the increase in scholarly research output produced. In order to facilitate discoverability of digital content in IRs, accurate, consistent and comprehensive association of descriptive metadata to digital objects during ingestion into IRs is crucial. However, due to human errors resulting from complex IR ingestion workflows, most digital content in IRs have incorrect and inconsistent descriptive metadata. While there exists a broad spectrum of descriptive metadata elements, subject headings present a classic example of a crucial metadata element that adversely affects discoverability of digital content when incorrectly and inconsistently specified. 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 a noticeable number of digital content is associated with incorrect subject categories and, additionally, associated with few subjects headings: two or less subject headings (71.2%), with a significant number of subject headings (92.1%) being associated with a single publication. A comparative study conducted 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). Furthermore, the effectiveness of the multi-label arXiv subjects classifier demonstrates the viability of integrating automated techniques for subject classification. |
Author | Nawila, Daisy M’sendo, Robert Phiri, Lighton Mwanza, Philomena Liswaniso, Eric Mawila, Andrew Nyirenda, Mayumbo Chipangila, Bertha |
Author_xml | – sequence: 1 givenname: Bertha surname: Chipangila fullname: Chipangila, Bertha organization: Department of Library and Information Science, University of Zambia – sequence: 2 givenname: Eric surname: Liswaniso fullname: Liswaniso, Eric organization: Department of Library and Information Science, University of Zambia – sequence: 3 givenname: Andrew surname: Mawila fullname: Mawila, Andrew organization: Department of Library and Information Science, University of Zambia – sequence: 4 givenname: Philomena surname: Mwanza fullname: Mwanza, Philomena organization: Department of Library and Information Science, University of Zambia – sequence: 5 givenname: Daisy surname: Nawila fullname: Nawila, Daisy organization: Department of Library and Information Science, University of Zambia – sequence: 6 givenname: Robert surname: M’sendo fullname: M’sendo, Robert organization: Department of Computer Science, University of Zambia – sequence: 7 givenname: Mayumbo surname: Nyirenda fullname: Nyirenda, Mayumbo organization: Department of Computer Science, University of Zambia – sequence: 8 givenname: Lighton orcidid: 0000-0003-3582-9866 surname: Phiri fullname: Phiri, Lighton email: lighton.phiri@unza.zm organization: Department of Library and Information Science, University of Zambia |
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Cites_doi | 10.3163/1536-5050.100.3.007 10.1177/0165551510386173 10.1080/01639370902737240 10.1045/january2005-foster 10.1186/1687-4722-2011-426793 10.28945/401 10.48550/ARXIV.1804.00566 10.5281/zenodo.4642379 10.5860/lrts.63n1.62 10.1504/IJMSO.2020.112804 10.1155/2017/8051673 10.1016/j.ipm.2013.05.003 10.1045/february97-arms 10.17487/rfc2413 10.4103/0019-5413.139827 10.4018/jdwm.2007070101 10.18653/v1/W19-2209 10.5281/zenodo.1212303 10.5860/lrts.53n3.174 10.1080/0968776042000211494 10.1080/01639374.2017.1358786 10.1002/asi.21336 |
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