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ПЕРСПЕКТИВИ / МОЖЛИВОСТІ ЗАСТОСУВАННЯ CHAT GPT ДЛЯ СТВОРЕННЯ МЕТАДАНИХ БІБЛІОТЕЧНИХ РЕСУРСІВ
Open accessAbstract
Advanced natural language processing technologies empower the development of intelligent systems capable of analyzing and comprehending text across various domains, including scientific publications, books, and other informational resources. This paper delves into the potential application of the artificial intelligence model ChatGPT within academic libraries, specifically focusing on metadata creation processes for library resources. Given the substantial resource investment required for metadata creation, description, cataloging, and adherence to globally recognized standards, the viability of leveraging modern technologies for such tasks is paramount. ChatGPT offers broad applicability in automating and enhancing the cataloging and description process of diverse documents. Serving as an artificial intelligence-based text generation system, ChatGPT can automatically generate metadata from textual descriptions of resources using sophisticated natural language processing algorithms. This capability streamlines and expedites resource cataloging processes, ultimately assisting library staff and information technology professionals in saving valuable time and effort. Furthermore, this paper discusses the potential of ChatGPT in generating metadata for various material types, ranging from scientific articles to websites and audio-visual recordings, while adhering to standards such as Dublin Core, MARC 21, and UNIMARK. Additionally, the article addresses potential challenges and limitations associated with using the ChatGPT model for metadata generation, including considerations of accuracy and text quality. Overall, this article explores a crucial aspect of applying artificial intelligence in the library field and highlights features and considerations concerning the use of the ChatGPT model for automatic metadata generation in library resources.
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“However, there is practically no research on integrating AI into library processes [1,12].”
From Contemporary approaches to integrating AI agents into library information processes (2025) · cites this paper as [1]
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- [12]12. Zhang, Y., Callan, J., & Minka, T. (2020). Machine Learning for Metadata Selection. "Proceedings of the ACM/IEEE Joint Conference on Digital Libraries", 207-216.
- [13]13. Miksa, T., & Ferus, A. (2019). Artificial Neural Networks for Metadata Creation. "International Conference on Theory and Practice of Digital Libraries", 228-239.
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- [17]17. Коваленко, І. О., & Григоренко, О. В. (2022). Використання мовних моделей для підтримки академічного процесу в Україні. "Наукові записки Національного університету «Острозька академія». Серія: Філософія", 30(2), 56-61.
- [24]24. K. Ganter, G. Neher, F. Schade, (2018), Die digitale Transformation meistern Aktuelle Entwicklungen in der bibliotheks-und informationswissenschaftlichen Aus-und Weiterbildung. "Bibliothek Forschung und Praxis", 42 (3), 441-452.
- [25]25. Gao, J., Galley, M., Li, L., et al. (2019). Neural Approaches to Conversational AI. "Proceedings of the IEEE", 107(8), 1527-1572.
- [26]26. Brown, P., Mann, B., Ryder, N., et al. (2020). Language Models are Few-Shot Learners. "Advances in Neural Information Processing Systems", 33.
- [27]27. Wang, A., Pruksachatkun, Y., Nangia, N., et al. (2020). Superglue: A Stickier Benchmark for General-Purpose Language Understanding Systems. "Proceedings of the 37th International Conference on Machine Learning".
- [29]29. Koolen, M., & Smith, M. (2020). AI and Metadata: A Survey of the State of the Art. "Journal of Documentation", 76(6), 1345-1366.
- [30]30. Zhang, Y., Callan, J., & Minka, T. (2020). Machine Learning for Metadata Selection. "Proceedings of the ACM/IEEE Joint Conference on Digital Libraries", 207-216.
- [31]31. Miksa, T., & Ferus, A. (2019). Artificial Neural Networks for Metadata Creation. "International Conference on Theory and Practice of Digital Libraries", 228-239.
- [32]32. Hswe, P., & Wallis, J. (2020). Using Language Models for Metadata Enhancement. "Journal of the Association for Information Science and Technology", 71(3), 282-295.
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Cited by
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Contemporary approaches to integrating AI agents into library information processes
Sokil, M., & Andrukhiv, A. (2025) · Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska · doi:10.35784/iapgos.6897 · Cited by 2
Metadata
- Authors
- СОКІЛ, М., & АНДРУХІВ, А.
- Affiliations
- Lviv Polytechnic National University (UA)
- Venue
- Herald of Khmelnytskyi National University. Technical sciences
- Publisher
- Khmelnytskyi National University
- Published
- 2024-10-31
- Volume
- 341
- Issue
- 5
- Pages
- 417-422
- ISSN
- 2307-5732 (ISSN-L)
- OpenAlex
- W4404968007
- Cited by
- 1
- References
- 0
- Citations by year
- 2025: 1
- FWCI
- 0.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Herald of Khmelnytskyi National University Technical sciences (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Library Science and Information Systems
- Social Sciences › Library and Information Sciences › Library Science and Information
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- ChatGPT, automatic metadata generation, library cataloging, academic libraries, natural language processing, Dublin Core, MARC 21, UNIMARC, artificial intelligence in libraries, metadata standards
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