This study explores the growth, trends and patterns of scholarly research by using bibliometric and computational methods. This study aims to identify prolific authors, influential journals, collaborative networks and emerging themes. This paper offers insights into how Artificial Intelligence (AI) and Machine Learning (ML) are transforming metadata practices to enhance information organisation, retrieval and interoperability.
A structured three-phase methodology was adopted. At first, appropriate keywords were identified, and the required data was extracted from the Scopus database (2010–2024) using a Boolean search string. Of 3,624 records, 3,424 articles were found relevant after removal of irrelevant articles manually. Software such as Biblioshiny and VOSviewer were used to analyse the trends in publication, citation patterns, co-occurrence of keywords and the collaborative patterns. Social Network Analysis was applied to map the co-authorship patterns, collaboration at institutional and country levels.
The results of this study indicate steady growth in AI and ML research in library metadata, though citation impact has declined since 2018. Kanwal Ameen is the most productive author, and the USA is the most productive country. Dominance by a few journals and authors aligns with Bradford’s and Lotka’s Laws. The collaboration pattern remains limited among authors and countries. Thematic evolution shows a shift from traditional metadata access to intelligent technologies like AI, ML and IoT. Regional collaborations are stronger than author-level links, and recent trends align with broader digital transformation
To the best of the authors’ knowledge, this is the first known study to comprehensively examine AI and ML applications in library metadata management through computational bibliometric analysis.
