Artificial intelligence (AI) tools have become essential in supporting cataloging and metadata enrichment in libraries, helping to streamline workflows and improve metadata quality. Among the leading tools, Alma by Ex Libris stands out with its AI Metadata Assistant, which leverages generative AI to suggest relevant metadata for cataloging. This includes creating and enriching MARC 21 bibliographic records, processing both physical and digital content types, and validating subject headings against standardized vocabularies such as the Library of Congress. Library professionals can review and adjust AI-generated suggestions to ensure accuracy and enhance metadata completeness [1], [2].
Another noteworthy tool is Annif, widely used for automated subject indexing and classification, notably by the National Library of Finland. Although subject heading prediction accuracy remains an area for improvement, Annif demonstrates considerable potential for tasks like retro-cataloging and identifying bibliographic records with enhanced efficiency [3], [4].
Large Language Models (LLMs) have also been applied experimentally to automate metadata generation tasks such as titles, authors, subjects, and descriptions. While these models show promise in token classification, they still require significant refinement, especially for precise subject heading assignments [4].
AI-powered metadata enrichment systems utilize natural language processing and semantic analysis techniques to generate detailed metadata, including keywords and entities, linked to standardized taxonomies. These enriched metadata sets enhance discoverability within academic and research contexts by connecting works with related datasets and citations [5].
Data catalog tools such as Collibra, Amundsen, Marquez, and DataHub incorporate AI and machine learning to automate metadata harvesting, classification, and data lineage tracking. Though primarily designed for data repositories, these platforms' automation and governance capabilities are increasingly relevant to library metadata management [6], [7], [8].
Recent advances in AI research focus on federated intelligence of LLMs to automate archival description and improve metadata catalog searches, representing a front line of innovation in library cataloging [9], [10].
In conclusion, libraries are leveraging hybrid approaches that combine domain expert oversight with sophisticated AI-driven metadata generation and enrichment tools to handle expanding collections efficiently and enhance user discovery experiences. Alma’s AI assistant currently leads in practical application, supplemented by tools like Annif and ongoing research into LLMs that promise further advancements in cataloging accuracy and efficiency.
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References:
[1] "6 best AI tools for librarians in 2025," Jotform, 2025. [Online]. Available: https://www.jotform.com/ai/agents/ai-tools-for-librarians/
[2] "The AI Metadata Assistant in the Metadata Editor," Ex Libris, 2025. [Online]. Available: https://knowledge.exlibrisgroup.com/Alma/Product_Documentation/010Alma_Online_Help_(English)/Metadata_Management/005Introduction_to_Metadata_Management/The_AI_Metadata_Assistant_in_the_Metadata_Editor
[3] "AI-supported cataloger: a deep dive into intelligent document classification," Emerald, 2025. [Online]. Available: https://www.emerald.com/lhtn/article/42/7/15/1270204/AI-supported-cataloger-a-deep-dive-into
[4] "How AI Will Transform Library Cataloging," Liblime, 2025. [Online]. Available: https://liblime.com/2025/10/11/how-ai-will-transform-library-cataloging/
[5] "AI Metadata Enrichment: Publishing Discoverability 2025," Luminadatamatics, 2025. [Online]. Available: https://www.luminadatamatics.com/resources/blog/why-ai-metadata-will-define-publishing-discoverability-in-2025/
[6] "Top 9 Data Catalog Tools in 2025," Integrate.io, 2025. [Online]. Available: https://www.integrate.io/blog/data-catalog-tools/
[7] "Top 26 Data Catalog Tools to Consider in 2025," LakeFS, 2025. [Online]. Available: https://lakefs.io/blog/top-data-catalog-tools/
[8] "18 Top Data Catalog Software Tools to Consider Using in 2025," TechTarget, 2025. [Online]. Available: https://www.techtarget.com/searchdatamanagement/feature/16-top-data-catalog-software-tools-to-consider-using
[9] "Automated Archival Descriptions with Federated Intelligence of LLMs," ArXiv, 2025. [Online]. Available: https://arxiv.org/pdf/2504.05711.pdf
[10] "Leveraging Retrieval Augmented Generative LLMs For Automated Metadata Description Generation to Enhance Data Catalogs," ArXiv, 2025. [Online]. Available: https://arxiv.org/pdf/2503.09003.pdf
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