Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

From Dewey to Deep Learning: Exploring the Intellectual Renaissance of Libraries through Artificial Intelligence


Affiliations
1 Librarian, Department of Libraries and Research, Srinagar – 190010, Jammu and Kashmir, India
     

   Subscribe/Renew Journal


Libraries are embracing the potential of Artificial Intelligence (AI) to enhance their services and provide more efficient and personalized experiences to users. This paper explores the role of AI in library services, focusing on its applications and impact. The present article begins by discussing the integration of AI technologies such as natural language processing, machine learning, and knowledge graphs in library systems. It then examines the benefits of AI, including improved information retrieval, recommendation systems, virtual assistants, and data analytics. Ethical considerations related to AI in libraries are also addressed. The paper highlights the challenges and future directions for AI implementation, including the need for training of librarians and the importance of user acceptance. The paper contributes to a better understanding of the opportunities and challenges associated with leveraging AI in library services, ultimately paving the way for more effective and user-centric library experiences.

Keywords

Artificial Intelligence, Information Retrieval, Knowledge Graphs, Libraries, Machine Learning, Natural Language Processing, Recommendation Systems
User
About The Author

Jan Mohd Mala
Librarian, Department of Libraries and Research, Srinagar – 190010, Jammu and Kashmir
India


Notifications

  • Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2022). Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges. Computer Science Review, 43, Article 100452. https://doi.org/10.1016/j. cosrev.2021.100452
  • Arora, A., Barrett, M., Lee, E., Oborn, E., & Prince, K. (2023). Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization. Information and Organization, 33(3), Article 100478. https://doi. org/10.1016/j.infoandorg.2023.100478
  • Bessis, N., & Dobre, C. (2014). Big data and internet of things: A roadmap for smart environments (Vol. 546). Basel, Switzerland: Springer International Publishing. https://doi. org/10.1007/978-3-319-05029-4
  • Boulos, M. N. K., Hetherington, L., & Wheeler, S. (2007). Second Life: An overview of the potential of 3‐D virtual worlds in medical and health education. Health Information and Libraries Journal, 24(4), 233-245. https://doi.org/10.1111/j.1471-1842.2007.00733.x PMid:18005298
  • Chakriswaran, P., Vincent, D. R., Srinivasan, K., Sharma, V., Chang, C. Y., & Reina, D. G. (2019). Emotion AI-driven sentiment analysis: A survey, future research directions, and open issues. Applied Sciences, 9(24), Article 5462. https://doi.org/10.3390/app9245462
  • Chen, S., Zhang, Y., Gu, X., & Liu, C. (2020). Training librarians in Artificial Intelligence: A case study of a summer school program. Journal of Academic Librarianship, 46(3), Article 102173.
  • Cortez, E., Bonde, A., Muzio, A., Russinovich, M., Fontoura, M., & Bianchini, R. (2017). Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms. Proceedings of the 26th Symposium on Operating Systems Principles (pp. 153-167). https://doi. org/10.1145/3132747.3132772
  • Covey, D. T. (2002). Usage and usability assessment: Library practices and concerns. Digital Library Federation.
  • Ding, Z., Jiang, S., Tang, J., Lin, Y. R., & Sun, J. (2019). Neural knowledge acquisition via mutual attention between knowledge graph and text. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 2109-2117).
  • Durge, A., Lokhande, A., Nagpure, A., Dharmik, C., & Dhawale, K. (2023). AI powered virtual voice assistant. International Research Journal of Modernization in Engineering Technology and Science, 5(11), 796-800.
  • Earley, S. (2020). The AI-powered enterprise: Harness the power of ontologies to make your business smarter, faster, and more profitable. LifeTree Media.
  • Eubanks, B. (2022). Artificial intelligence for HR: Use AI to support and develop a successful workforce. Kogan Page Publishers.
  • Farid, G., Warraich, N. F., & Iftikhar, S. (2023). Digital information security management policy in academic libraries: A systematic review (2010–2022). Journal of Information Science. https://doi.org/10.1177/01655515231160026
  • Gani, A., Siddiqa, A., Shamshirband, S., & Hanum, F. (2016). A survey on indexing techniques for big data: Taxonomy and performance evaluation. Knowledge and Information Systems, 46, 241-284. https://doi.org/10.1007/s10115-015- 0830-y
  • González-Blanco, E., Calzada-Prado, I., & Mendoza-González, R. (2018). Conversational Agents in Libraries: Analyzing the Ethical Issues. IFLA WLIC 2018 – 84th IFLA General Conference and Assembly, Kuala Lumpur, Malaysia.
  • Graser, M., & Burel, M. (2018). Metadata automation: The current landscape and future developments. Visual Resources Association Bulletin, 45(2).
  • Hahn, J. (2012). Mobile augmented reality applications for library services. New library world, 113(9/10), 429-438. https://doi.org/10.1108/03074801211273902
  • Hannah, M., Huber, S., & Matei, S. A. (2019). Collecting virtual and augmented reality in the twenty-first century library. Collection Management, 44(2-4), 277-295. https:// doi.org/10.1080/01462679.2019.1587673
  • Holub, E., & Sheble, M. (2021). Ethical considerations in Artificial Intelligence use in libraries. Global Librarian (pp. 127-134). Springer.
  • Hussain, F., Hassan, S. A., Hussain, R., & Hossain, E. (2020). Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges. IEEE Communications Surveys and Tutorials, 22(2), 1251-1275. https://doi.org/10.1109/ COMST.2020.2964534
  • Luca, E., & Ulyannikova, Y. (2020). Towards a user-centred systematic review service: The transformative power of service design thinking. Journal of the Australian Library and Information Association, 69(3), 357-374. https://doi.org /10.1080/24750158.2020.1760506
  • Nirala, K. K., Singh, N. K., & Purani, V. S. (2022). A survey on providing customer and public administration based services using AI: chatbot. Multimedia Tools and Applications, 81(16), 22215-22246. https://doi.org/10.1007/ s11042-021-11458-y PMid:35002470 PMCid:PMC8721490
  • Marr, B. (2019). Artificial intelligence in practice: how 50 successful companies used AI and machine learning to solve problems. John Wiley & Sons.
  • Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR), 54(6), 1-35. https://doi.org/10.1145/3457607
  • Mishra, S., & Tyagi, A. K. (2022). The role of machine learning techniques in internet of things-based cloud applications. Artificial Intelligence-Based Internet of Things Systems, 105-135. https://doi.org/10.1007/978-3-030-87059- 1_4
  • Jaeger, P. T., Gorham, U., & Bertot, J. C. (2018). AI and machine learning in libraries: Present and future uses. Library Hi Tech, 36(4), 676-689.
  • Jiang, C., Wang, J., & Lu, K. (2020). A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Knowledge and Data Engineering, 33(11), 4761-4786.
  • Kaplan, A. M., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25. https://doi.org/10.1016/j. bushor.2018.08.004
  • Kaur, H. (2023). Where are the humans in Human-AI interaction: The missing human-centered perspective on interpretability tools for machine learning [Doctoral dissertation].
  • Lekadir, K., Osuala, R., Gallin, C., Lazrak, N., Kushibar, K., Tsakou, G., ... & Martí-Bonmatí, L. (2021). FUTURE-AI: guiding principles and consensus recommendations for trustworthy artificial intelligence in medical imaging. arXiv preprint arXiv:2109.09658. https://doi.org/10.48550/arXiv.2109.09658
  • Liu, X., Zhang, X., Wang, T., & Zhao, X. (2021). Implementation of augmented reality in libraries: A study of user experiences and user acceptance. Journal of Librarianship and Information Science, 53(1), 141-157.
  • Panda, S., & Kaur, N. (2023). Enhancing user experience and accessibility in digital libraries through emerging technologies. Conference: International Symposium on Digital Libraries: Sustainable Development in Education (DLSDE-2023)At: IIT Kharagpur, West Bengal, India
  • Raghavendra, C. K., Srikantaiah, K. C., & Venugopal, K. R. (2018). Personalized recommendation systems (PRES): A comprehensive study and research issues. International Journal of Modern Education and Computer Science, 11(10). https://doi.org/10.5815/ijmecs.2018.10.02
  • Rane, N. (2023). Enhancing customer loyalty through Artificial Intelligence (AI), Internet of Things (IoT), and Big Data technologies: Improving customer satisfaction, engagement, relationship, and experience. SSRN. https:// doi.org/10.2139/ssrn.4616051
  • Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.
  • Saravanan, M., Ravindran, B., & Raman, S. (2009). Improving legal information retrieval using an ontological framework. Artificial Intelligence and Law, 17, 101-124. https://doi.org/10.1007/s10506-009-9075-y
  • Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5, 115-153. https://doi. org/10.1023/A:1009804230409
  • Schiff, D., Rakova, B., Ayesh, A., Fanti, A., & Lennon, M. (2020). Principles to practices for responsible AI: closing the gap. arXiv preprint arXiv:2006.04707. https://doi.org/10.48550/arXiv.2006.04707
  • Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022). Towards a standard for identifying and managing bias in artificial intelligence. NIST Special Publication. https://nvlpubs.nist.gov/nistpubs/SpecialPublications/ NIST.SP.1270.pdf
  • Shao, Z., Li, Y., Huang, P., Abed, A. M., Ali, E., Elkamchouchi, D. H., ... & Zhang, G. (2023). Analysis of the opportunities and costs of energy saving in lightning system of library buildings with the aid of building information modelling and Internet of things. Fuel, 352, Article 128918. https:// doi.org/10.1016/j.fuel.2023.128918
  • Thoenssen, B. (2013). Automatic, format-independent generation of metadata for documents based on semantically enriched context information. Semantic Scholar.
  • Van Hooland, S., & Verborgh, R. (2014). Linked Data for Libraries, Archives and Museums: How to clean, link and publish your metadata. Facet publishing. https://doi. org/10.29085/9781783300389
  • Verma, S. (2022). Sentiment analysis of public services for smart society: Literature review and future research directions. Government Information Quarterly, 39(3), Article 101708. https://doi.org/10.1016/j.giq.2022.101708
  • Verma, V. K., & Gupta, S. (2022). Artificial Intelligence and the Future Libraries. World Digital Libraries, 15(2), 151-166.
  • Wu, D., & Chang, Y. (2021). The future role of Artificial Intelligence (AI) in academic libraries: A literature review and future research directions. College and Research Libraries, 82(1), 43-60.
  • Xu, F., & Du, J. T. (2018). Factors influencing users’ satisfaction and loyalty to digital libraries in Chinese universities. Computers in Human Behavior, 83, 64-72. https://doi.org/10.1016/j.chb.2018.01.029
  • Youssef, H. A. H., & Hossam, A. T. A. (2023). Privacy issues in AI and cloud computing in e-commerce setting: A review. International Journal of Responsible Artificial Intelligence, 13(7), 37-46
  • Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex and Intelligent Systems, 7, 439-457. https://doi.org/10.1007/s40747-020-00212-w

Abstract Views: 106

PDF Views: 3




  • From Dewey to Deep Learning: Exploring the Intellectual Renaissance of Libraries through Artificial Intelligence

Abstract Views: 106  |  PDF Views: 3

Authors

Jan Mohd Mala
Librarian, Department of Libraries and Research, Srinagar – 190010, Jammu and Kashmir, India

Abstract


Libraries are embracing the potential of Artificial Intelligence (AI) to enhance their services and provide more efficient and personalized experiences to users. This paper explores the role of AI in library services, focusing on its applications and impact. The present article begins by discussing the integration of AI technologies such as natural language processing, machine learning, and knowledge graphs in library systems. It then examines the benefits of AI, including improved information retrieval, recommendation systems, virtual assistants, and data analytics. Ethical considerations related to AI in libraries are also addressed. The paper highlights the challenges and future directions for AI implementation, including the need for training of librarians and the importance of user acceptance. The paper contributes to a better understanding of the opportunities and challenges associated with leveraging AI in library services, ultimately paving the way for more effective and user-centric library experiences.

Keywords


Artificial Intelligence, Information Retrieval, Knowledge Graphs, Libraries, Machine Learning, Natural Language Processing, Recommendation Systems

References





DOI: https://doi.org/10.17821/srels%2F2024%2Fv61i1%2F171001