Natural Language Processing (NLP) Application For Classifying and Managing Tacit Knowledge in Revolutionizing AI-Driven Library
Abstract
The rapid evolution of technology has transformed library systems, with Natural Language Processing (NLP) emerging as a pivotal tool for enhancing knowledge management. This study aims to examine how NLP can improve the classification and management of tacit knowledge within AI-driven libraries, addressing the challenge of handling large volumes of unstructured data. The objective is to explore how NLP can optimize the retrieval, organization, and access to tacit knowledge, thus enhancing decision-making processes in libraries. The research adopts a conceptual design, synthesizing existing literature and theoretical models, including Information Processing Theory and Constructivist Theory, to propose a framework that integrates NLP with traditional knowledge management practices. Methodologies include a thorough review of recent advancements in NLP technologies and their applications within knowledge management systems. The study’s findings demonstrate that NLP significantly improves the accuracy and efficiency of knowledge retrieval by automating the processing of natural language data. This allows better access to tacit knowledge, supporting more informed decision-making. The outcomes of the study are twofold: it enhances existing knowledge management frameworks theoretically, and it provides practical insights for libraries to leverage NLP for greater operational efficiency and improved user experience. The study also underscores the need for future research on the real-world application of NLP and its ethical implications, such as data privacy and algorithmic bias.
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Ambalavanan, A. K., & Devarakonda, M. V. (2020). Using the contextual language model BERT for multi-criteria classification of scientific articles. Journal of Biomedical Informatics, 112, 103578. DOI: https://doi.org/10.1016/j.jbi.2020.103578
BERNAMA. (2024, October 10). Malaysia's growth forecast revised to 4.5% amid global challenges. BERNAMA. https://www.bernama.com/en/news.php?id=2349055
Borovic, R., Marinic, A., & Zivkovic, D. (2022). A hybrid approach to Universal Decimal Classification (UDC) code recommendation using BM25 and BERT-based classifiers. Journal of Library Technology, 15(3), 120-133.
Crabtree, M. (2023). What is natural language processing (NLP)? A comprehensive guide for beginners. DataCamp. https://www.datacamp.com/blog/what-is-natural-language-processing
Crowston, K., Allen, E. E., & Heckman, R. (2012). Using natural language processing technology for qualitative data analysis. International Journal of Social Research Methodology, 15(6), 523-543. DOI: https://doi.org/10.1080/13645579.2011.625764
Chen, Y., Huang, X., & Li, Y. (2022). The role of tacit knowledge in innovation: A resource-based view perspective. Journal of Business Research, 140, 456-467.
Chen, Y., Wang, H., Yu, K., & Zhou, R. (2024). Artificial Intelligence Methods in Natural Language Processing: A Comprehensive Review. Highlights in Science, Engineering and Technology, 85, 545-550. DOI: https://doi.org/10.54097/vfwgas09
Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649. DOI: https://doi.org/10.1007/978-81-322-3972-7_19
Dash, B. (2022). Information Extraction from Unstructured Big Data: A Case Study of Deep Natural Language Processing in Fintech. University of the Cumberlands.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges, and implications of generative conversational AI for research, practice, and policy. International Journal of Information Management, 71, 102642. DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102642
Fanni, S. C., Febi, M., Aghakhanyan, G., & Neri, E. (2023). Natural language processing. In Introduction to Artificial Intelligence (pp. 87-99). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-031-25928-9_5
Farid, M. (2024, April 18). Exploring the triad of AI: Machine learning, NLP, and their synergy. LinkedIn. https://www.linkedin.com/pulse/exploring-triad-ai-machine-learning-nlp-synergy-mazhar-farid-rg5nf
Fortune Business Insights. (2024). Natural language processing (NLP) market size, share & industry analysis by component (solutions, services), by deployment (on-premise, cloud), by industry vertical (healthcare, retail & e-commerce, financial services, IT & telecom, others), and regional forecast, 2023-2032.
Garcia, R., & Smith, J. (2023). Participatory classification in library science: Empowering contributors and enhancing accuracy. Journal of Knowledge Management, 27(3), 215-232. https://doi.org/10.1108/JKM-03-2023-0154
Hernandez, L., & Martinez, P. (2019). Integrating NLP in library classification systems: Multilingual contexts and beyond. Library and Information Science Research, 41(2), 102-113. https://doi.org/10.1016/j.lisr.2019.03.002 DOI: https://doi.org/10.1016/j.lisr.2019.03.002
Hossain, M. B., Miraz, M. H., & Ya'u, A. (2024). FROM LEGALITY TO RESPONSIBILITY: CHARTING THE COURSE FOR AI REGULATION IN MALAYSIA. IIUM Law Journal, 32(1), 397-429. DOI: https://doi.org/10.31436/iiumlj.v32i1.927
IBM. (2024, August 11). What is NLP (Natural Language Processing)? IBM. https://www.ibm.com/topics/natural-language-processing
International Federation of Library Associations and Institutions. (2023). IFLA annual report 2023. IFLA. https://cdn.ifla.org/wp-content/uploads/IFLA-Annual-Report-2023-text-version.pdf
Jin, K., & Zhuo, H. H. (2022). Integrating AI planning with natural language processing: a combination of explicit and tacit knowledge. arXiv preprint arXiv:2202.07138.
Jyoti, & Kumar, P. (2024). Reshaping the library landscape: Exploring the integration of artificial intelligence in libraries. International Journal of Library and Information Technology. https://www.ijlsit.org/html-article/22422 DOI: https://doi.org/10.18231/j.ijlsit.2024.005
Johnson, L., & Williams, K. (2024). Ethical considerations in automated classification systems. Journal of Library Ethics, 18(1), 54-70. https://doi.org/10.1177/1747016123111456 DOI: https://doi.org/10.1080/17496535.2023.2237219
Kadir, M., & Noor, S. (2022). User experiences with NLP tools in Malaysian academic libraries. Journal of Library & Information Services, 50(3), 178-195. https://doi.org/10.1080/15323269.2022.2055672
Kadir, A. Z., & Noor, S. M. (2022). Enhancing Information Retrieval in Malaysian Academic Through NLP Tools: A User Experience Study. Journal of Information Science, 48(2), 241-255. https://doi.org/10.1177/0165551521999974
Khan, W., Daud, A., Khan, K., Muhammad, S., & Haq, R. (2023). Exploring the frontiers of deep learning and natural language processing: A comprehensive overview of key challenges and emerging trends. Natural Language Processing Journal, 100026. DOI: https://doi.org/10.1016/j.nlp.2023.100026
Just, J. (2024). Natural language processing for innovation search–Reviewing an emerging non-human innovation intermediary. Technovation, 129, 102883. DOI: https://doi.org/10.1016/j.technovation.2023.102883
Kalisdha, A. (2024). The Impact of Artificial Intelligence and Machine Learning in Library and Information Science. DOI: https://doi.org/10.26761/ijrls.10.1.2024.1733
Kang, Y., Cai, Z., Tan, C. W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139-172. DOI: https://doi.org/10.1080/23270012.2020.1756939
Kim, J., Lee, D., & Park, H. (2022). Comparative analysis of NLP models: Traditional methods vs. BERT. Journal of Artificial Intelligence Research, 17(3), 140-155.
Kurdi, G. (2022). Toward an Electronic Resource for Systematic Reviews in Computing. Available at chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.researchgate.net/profile/Ghader-Kurdi/publication/361779982_Toward_an_Electronic_Resource_for_Systematic_Reviews_in_Computing/
Lee, H., & Kim, S. (2022). Addressing the limitations of NLP in capturing the subtleties of AI-driven Library experiences. Knowledge Management Research & Practice, 20(4), 334-347. https://doi.org/10.1080/14778238.2021.1993475
Lee, J., & Kim, Y. (2024). Enhancing knowledge sharing with NLP: Applications in organizational settings. Journal of Knowledge Management, 28(2), 195-210. https://doi.org/10.1108/JKM-12-2023-0491
Li, Q., Peng, H., Li, J., Xia, C., Yang, R., Sun, L., ... & He, L. (2020). A survey on text classification: From shallow to deep learning. arXiv preprint arXiv:2008.00364
LibLime. (2023, December 4). Revolutionizing library cataloging with artificial intelligence (AI). LinkedIn. https://www.linkedin.com/pulse/revolutionizing-library-cataloging-artificial-intelligence-ai-coo9c
Lim, C., & Ibrahim, N. (2021). Case study on the impact of NLP applications on unstructured data management. Journal of Academic Librarianship, 47(6), 102341. https://doi.org/10.1016/j.acalib.2021.102341
Lim, K. H., & Ibrahim, N. (2021). Application of NLP in Digital Library Platforms: A Case Study in Malaysia. Library Hi Tech, 39(4), 965-981. https://doi.org/10.1108/LHT-08-2020-0205 DOI: https://doi.org/10.1108/LHT-08-2020-0205
Mahadevkar, S. V., Patil, S., Kotecha, K., Soong, L. W., & Choudhury, T. (2024). Exploring AI-driven approaches for unstructured document analysis and future horizons. Journal of Big Data, 11(1), 92. DOI: https://doi.org/10.1186/s40537-024-00948-z
Malaysia Digital Economy Corporation. (2023). Malaysia’s Digital Economy to Contribute 25.5% to GDP by 2025. MDEC. https://mdec.my/digital-economy/
Manousaridis, J. (2024, September 24). The role of natural language processing in enhancing knowledge management systems. Mindbreeze.
Nguyen, D. N. & Bui, T. L. (2023). Review of higher education policies during pandemic Covid-19: A Vietnamese perspective. Journal for Educators, Teachers and Trainers, 14(4), 138-149. DOI: https://doi.org/10.47750/jett.2023.14.04.013
Nguyen, A., Li, Y., & Chan, W. (2024). Enhancing knowledge retrieval efficiency through NLP: A case study in AI-driven libraries. Journal of Information Technology and Libraries, 32(4), 201-216.
Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press. DOI: https://doi.org/10.1093/oso/9780195092691.001.0001
Noor, M., & Rana, Z. A. (2023, February). Natural Language Processing (NLP) based extraction of tacit knowledge from written communication during software development. In 2023 4th International Conference on Advancements in Computational Sciences (ICACS) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICACS55311.2023.10089779
Panda, Subhajit & Kaur, Navkiran (2023). Enhancing User Experience and Accessibility in Digital Libraries through Emerging Technologies, In K.P.
Panda, S., & Bhattacharya, S. (2023). Leveraging NLP for library metadata management: A systematic approach. Journal of Digital Information Management, 21(2), 124-140. https://doi.org/10.1145/3512290 DOI: https://doi.org/10.1145/3512290
Patel, A., Mehta, R., & Singh, P. (2023). The impact of Natural Language Processing on tacit knowledge management in libraries. Journal of Knowledge Management, 27(3), 189-205.
Patel, S., & Desai, R. (2023). The role of NLP in decision-making and tacit knowledge classification. Journal of Knowledge Management, 28(5), 320-332.
Pauzi, Z., & Capiluppi, A. (2023). Applications of natural language processing in software traceability: A systematic mapping study. Journal of Systems and Software, 198, 111616. DOI: https://doi.org/10.1016/j.jss.2023.111616
Roy, S., Vijay Mallikraj, S., Moradia, S., Shantha, G., Aravind, S., & Shivaprakash, S. (2024). The impact of artificial intelligence on cataloging and classification systems in modern libraries. LIB PRO, 44(3), JUL-DEC 2024. https://bpasjournals.com/library-science/index.php/journal/article/view/468
Sanchez-Segura, M. I., González-Cruz, R., Medina-Dominguez, F., & Dugarte-Peña, G. L. (2022). Valuable business knowledge asset discovery by processing unstructured data. Sustainability, 14(20), 12971. DOI: https://doi.org/10.3390/su142012971
Semeler, A. R., Pinto, A. L., & Rozados, H. B. F. (2019). Data science in data librarianship: Core competencies of a data librarian. Journal of librarianship and information science, 51(3), 771-780. DOI: https://doi.org/10.1177/0961000617742465
Singh, R., Sharma, N., & Gupta, K. (2023). Case studies of successful NLP applications in knowledge management within libraries. International Journal of Library and Information Services, 35(2), 99-115.
Singh, R., Gupta, S., & Khanna, P. (2023). NLP for knowledge sharing in modern organizations: A practical case study. Journal of Knowledge Management Systems, 15(1), 55-71.
Smith, R., & Jones, L. (2023). The complexity of classifying AI-driven Library experiences: Challenges and opportunities. Library Trends, 71(4), 554-569. https://doi.org/10.1353/lib.2023.0028
Song, X. (2022). Enhancing text classification accuracy in bilingual datasets using weight preprocessing. Computational Linguistics Journal, 48(2), 101-115.
Tahri, C. (2023). Leveraging Modern Information Seeking on Research Papers for Real-World Knowledge Integration Applications: An Empirical Study (Doctoral dissertation, Sorbonne Université).
Von Krogh, G., Ichijo, K., & Nonaka, I. (2000). Enabling knowledge creation: How to unlock the mystery of tacit knowledge and release the power of innovation. Oxford University Press. DOI: https://doi.org/10.1093/acprof:oso/9780195126167.001.0001
Wakuthii, S. (2023). AI and ML's role in extracting tacit knowledge gems. LinkedIn. https://www.linkedin.com/pulse/ai-mls-role-extracting-tacit-knowledge-gems-sarah-wakuthii
Wolf, T., Debut, L., Sanh, V., Chaumond, J., & Delangue, C. (2020). Transformers: State-of-the-art NLP models for various tasks. Proceedings of the International Conference on Natural Language Processing, 34(5), 45-57.
Wu, S., Roberts, K., Datta, S., Du, J., Ji, Z., Si, Y., ... & Xu, H. (2020). Deep learning in clinical natural language processing: a methodical review. Journal of the American Medical Informatics Association, 27(3), 457-470. DOI: https://doi.org/10.1093/jamia/ocz200
Xu, K., Zhou, H., Zheng, H., Zhu, M., & Xin, Q. (2024). Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning. arXiv preprint arXiv:2403.19345. DOI: https://doi.org/10.54254/2755-2721/64/20241365
Zala, K., Acharya, B., Mashru, M., Palaniappan, D., Gerogiannis, V. C., Kanavos, A., & Karamitsos, I. (2024). Transformative Automation: AI in Scientific Literature Reviews. International Journal of Advanced Computer Science & Applications, 15(1). DOI: https://doi.org/10.14569/IJACSA.2024.01501122
Zhang, T., & Lu, Q. (2022). Advances and challenges in automating tacit knowledge classification using NLP. Knowledge Discovery and Information Systems, 18(3), 112-127.
Zhou, X., & Liang, S. (2023). The role of BERT in reducing search time: A study of NLP’s efficiency in knowledge retrieval. Library and Information Science Research, 41(1), 56-72.
Zhou, X., Wang, S., & Yang, H. (2023). Reducing search time by 40%: The impact of NLP on knowledge management. International Journal of Information Systems, 21(2), 145-159.
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