SMART METADATA MANAGEMENT FOR PRINT ARCHIVES
DOI:
https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6839Keywords:
Smart Metadata Management, Digital Archives, Artificial Intelligence (AI), Optical Character Recognition (OCR), Natural Language Processing (NLP), Semantic Enrichment and Knowledge GraphsAbstract [English]
Metacognitive metadata management of print archives is one of the essential steps that must be taken to integrate the old archival methodology with the new digital intelligence. With the shift of the cultural institution and libraries towards dynamic digital environments, replacing the static cataloging systems with the dynamic digital ecosystems, the needs of making the metadata efficient, accurate, and information-rich increases manifold. This paper will describe an intelligent model of metadata management, which uses Artificial Intelligence (AI), Optical Character Recognition (OCR), Natural Language Processing (NLP), and linked data frameworks to perform intelligent work on archives. The framework suggested combines automated metadata extraction, real-time validation, semantic enrichment, and entity recognition based on machine learning pipelines that are compatible with existing metadata standards (dublin core, MARC, METS and PREMIS). A prototype implementation presents the ways AI-based workflows can be applied to facilitate metadata completeness, interoperability, and retrieval accuracy in digitized print repositories. Scanned archival data has been evaluated through experimental evaluations where there is a significant difference in accuracy and efficiency compared to traditional manual cataloging systems. Performance insights, system architecture, scalability and semantic alignment issues and long-term preservation implications are also addressed in the study. Finally, the study will add a model of the future that unites automation with semantic intelligence, improves discoverability, sustainability, and accessibility of cultural heritage collections with smart metadata governance and intelligent interoperability of archival.
References
Chakraborty, D., and Paul, J. (2023). Healthcare Apps’ Purchase Intention: A Consumption Values Perspective. Technovation, 120, Article 102481. https://doi.org/10.1016/j.technovation.2022.102481
De Aguiar, E. J., Faiçal, B. S., Krishnamachari, B., and Ueyama, J. (2020). A Survey of Blockchain-Based Strategies for Healthcare. ACM Computing Surveys, 53(2), 1–27. https://doi.org/10.1145/3376915
Houkat, M. U., Yan, L., Yan, Y., Zhang, F., Zhai, Y., Han, P., Nawaz, S. A., Raza, M. A., Akbar, M. W., and Hussain, A. (2024). Autonomous Driving Test System Under Hybrid Reality: The Role of Digital Twin Technology. Internet of Things, 27, Article 101301. https://doi.org/10.1016/j.iot.2024.101301
Hu, F., Qiu, L., and Zhou, H. (2022). Medical Device Product Innovation Choices in Asia: An Empirical Analysis based on Product Space. Frontiers in Public Health, 10, Article 871575. https://doi.org/10.3389/fpubh.2022.871575
Liu, Y., Ju, F., Zhang, Q., Zhang, M., Ma, Z., Li, M., and Liu, F. (2023). Overview of Internet of Medical Things Security Based on Blockchain Access Control. Journal of Database Management, 34(1), 1–20. https://doi.org/10.4018/JDM.321545
Rahman, M. S., Islam, M. A., Uddin, M. A., and Stea, G. (2022). A Survey of Blockchain-Based IoT Ehealthcare: Applications, Research Issues, and Challenges. Internet of Things, 19, Article 100551. https://doi.org/10.1016/j.iot.2022.100551
Rani, S., Jining, D., Shah, D., Xaba, S., and Shoukat, K. (2024). Examining the Impacts of Artificial Intelligence Technology and Computing on Digital Art: A Case Study of Edmond de Belamy and its Aesthetic Values and Techniques. AI and Society. Advance Online Publication. https://doi.org/10.1007/s00146-024-01996-y
Rejeb, A., Rejeb, K., Treiblmaier, H., Appolloni, A., Alghamdi, S., Alhasawi, Y., and Iranmanesh, M. (2023). The Internet of Things (IoT) in Healthcare: Taking Stock and Moving Forward. Internet of Things, 22, Article 100721. https://doi.org/10.1016/j.iot.2023.100721
Shoukat, K., Jian, M., Umar, M., Kalsoom, H., Sijjad, W., Atta, S. H., and Ullah, A. (2023). Use of Digital Transformation and Artificial Intelligence Strategies for Pharmaceutical Industry in Pakistan: Applications and Challenges. Artificial Intelligence in Health, 1, Article 1486. https://doi.org/10.36922/aih.1486
Shoukat, M. U., Yan, L., Zhang, J., Cheng, Y., Raza, M. U., and Niaz, A. (2024). Smart Home for Enhanced Healthcare: Exploring Human–Machine Interface Oriented Digital Twin Model. Multimedia Tools and Applications, 83, 31297–31315. https://doi.org/10.1007/s11042-023-16875-9
Taylor, P. J., Dargahi, T., Dehghantanha, A., Parizi, R. M., and Choo, K. K. R. (2020). A Systematic Literature Review of Blockchain Cyber Security. Digital Communications and Networks, 6(2), 147–156. https://doi.org/10.1016/j.dcan.2019.01.005
Uddin, M. A., Stranieri, A., Gondal, I., and Balasubramanian, V. (2021). A Survey on the Adoption of Blockchain in IoT: Challenges and Solutions. Blockchain: Research and Applications, 2(3), Article 100006. https://doi.org/10.1016/j.bcra.2021.100006
Wang, X., Zha, X., Ni, W., Liu, R. P., Guo, Y. J., Niu, X., and Zheng, K. (2019). Survey on Blockchain for Internet of Things. Computer Communications, 136, 10–29. https://doi.org/10.1016/j.comcom.2019.01.006
Yaqoob, I., Salah, K., Jayaraman, R., and Al-Hammadi, Y. (2021). Blockchain for Healthcare Data Management: Opportunities, Challenges, and Future Recommendations. Neural Computing and Applications, 34, 11475–11490. https://doi.org/10.1007/s00521-020-05519-w
Zaman, U., Mehmood, F., Iqbal, N., Kim, J., and Ibrahim, M. (2022). Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications. Electronics, 11(12), Article 1893. https://doi.org/10.3390/electronics11121893
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Copyright (c) 2025 Mr. Rohit Chandwaskar, Sathyabalaji Kannan, Mr. Srikanta Kumar Sahoo, Rashmi Manhas, Takveer Singh, Ms. Usha Kiran barla
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