Talks | JCDL 2021 | Discoverability of Content Using Controlled Vocabularies & Machine Learning
Channel: Lighton Phiri
Date: 28/09/2021

Lighton Phiri gave a talk titled "Improved Discoverability of Digital Objects in Institutional Repositories Using Controlled Vocabularies" during the 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL 2021) [1], held between September 27-30, 2021. 

This talk is based on a peer-reviewed article published in the 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL 2021) Conference Proceedings [2] and is linked to research conducted as part of a final year undergraduate capstone project [3] and a Master's project. The presentation slides [4] and peer-reviewed article [5] are both publicly available.

## Video Timeline
00:00:00 Personal Introduction
00:00:15 About The DataLab Research Group
00:00:50 Presentation Outline
00:01:03 Motivation
00:02:01 Problem Statement
00:03:35 Methodology
00:06:00 Results and Discussion
00:09:28 Conclusion and Future Work


## Title
Improved Discoverability of Digital Objects in Institutional Repositories Using Controlled Vocabularies

## Abstract
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.

## Biography
Lighton Phiri is a Lecturer and Researcher in the Department of Library and Information Science at the University of Zambia [6], where he is a member of the DataLab research group [7]. His broad research interests lie within the areas of Data Mining, Digital Libraries and Technology-Enhanced Learning. He is currently a member of the Networked Digital Library of Theses and Dissertations (NDLTD) "Enhancement of the Union Catalog" Working Group [8] and also sits on the Association of Computing Machinery SIGIR/SIGKDD AFIRM steering committee [9]. 

## Bibliography
[1] https://2021.jcdl.org
[2] https://2021.jcdl.org
[3] http://datalab.unza.zm/publications/21
[4] https://www.slideshare.net/lightonphiri/effective-ingestion-of-digital-objects-in-institutional-repositories-using-subject-repositories
[5] https://drive.google.com/file/d/1rU_HCgcSDX2VyWO28Ta0xp8kLCidZhBc/view?usp=sharing
[6] https://www.unza.zm
[7] http://datalab.unza.zm
[8] http://www.ndltd.org/a/ndltd.org/ndltd/directory/committees-and-working-groups/enhancement-of-the-union-catalog
[9] http://sigir.org/afirm2020

#DigitalLibraries
#MachineLearning
#InstitutionalRepositories