Improved Discoverability of Digital Objects in Institutional Repositories Using Controlled Vocabularies

Bertha Chipangila, Eric Liswaniso, Andrew Mawila, Philomena Mwanza, Daisy Nawila, Robert M'sendo, Mayumbo Nyirenda, Lighton Phiri
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Abstract

Higher Education Institutions (HEIs) utilise Institutional 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.
利用受控词汇表改进机构知识库中数字对象的可发现性
高等教育机构(HEIs)利用机构知识库(IRs)以电子方式存储和提供教职员工和学生的学术研究成果。随着学术研究产出的不断增加,在导入IRs时,准确、全面地将主题标题与数字对象关联起来,对于有效地发现对象和促进相关内容的发现至关重要。本文概述了在赞比亚大学进行的一个案例研究,以证明在摄取数字对象时将受控主题词汇整合到ir中的有效性。通过情景分析来了解主题标题是如何与数字对象相关联的,并分析与已经摄入的数字对象相关联的主题标题。此外,还进行了一项探索性研究,以确定与IR集成的领域特定主题标题。此外,进行了一项可用性研究,以比较确定在将数字对象摄取到IRs时使用受控词汇的有用性。最后,进行多标签分类实验,为数字对象分配多个类别。研究结果显示,目前大多数数字对象与两个或更少的主题标题(71.2%)相关联,其中大量主题标题(92.1%)与单一出版物相关联。对比研究表明,与没有控制词汇表的IRs相比,集成了控制词汇表的IRs被认为更有用(SUS得分= 68.9)。多标签arXiv主题分类器的有效性证明了集成自动化主题分类技术的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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