Intelligent Metadata Architectures: AI-Driven and Knowledge Engineering for Next-Generation Digital Libraries

In today’s rapidly shifting digital environment, metadata forms the backbone of accessibility, interoperability, and long-term preservation in digital libraries. This study investigates the development of intelligent metadata architectures that merge Artificial Intelligence (AI) with Knowledge Engineering (KE) to advance next-generation library systems. The proposed framework integrates metadata curation, ontology construction, semantic web applications, and machine learning techniques to improve resource discovery, personalization, and cross-platform interoperability. Special attention is given to AI-driven metadata enrichment and the use of knowledge graphs for handling multilingual, multimedia, and heterogeneous collections, thereby expanding inclusivity and global access. The paper also considers challenges related to metadata standardization, digital preservation, and ethical issues, particularly intellectual property rights. Through selected case studies, it demonstrates how intelligent metadata systems can transform digital libraries into adaptive, sustainable, and user-focused infrastructures, aligning with the goals of the National Digital Library of India (NDLI) and contributing to the wider vision of globally connected educational resources.