NEURAL NETWORKS AND THE FUTURE OF KNOWLEDGE ORGANIZATION IN LIBRARIES

Abstract

This article examines the transformative potential of neural networks for the future of knowledge organization in libraries. Confronting the limitations of traditional, hierarchical systems like LCSH and DDC in the face of digital abundance, it argues for a new paradigm of human-AI collaboration. The analysis explores how deep learning models, through semantic embedding, automated metadata enhancement, and pattern recognition, can move cataloging beyond explicit description to capture implicit, contextual, and relational knowledge. This shift enables dynamic, user-centric organization, generating multiple, emergent taxonomies that reflect actual scholarly use and interdisciplinary connections. The paper critically addresses the essential role of librarian expertise in governing these systems, ensuring ethical application, mitigating bias, and maintaining professional values of equity and transparency. Ultimately, it posits that neural networks will not replace librarians but will augment their intellectuality, transforming the library into a platform for generative discovery and complex interpretive dialogue, thereby reinvigorating the core mission of librarianship in the digital age.