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This paper introduces a Smart OPAC framework that leverages AI and knowledge graphs to enhance traditional library catalogs. The framework integrates open scholarly data, applies semantic embeddings for improved relevance, and offers features like semantic search and thematic filtering. Evaluation shows improvements in retrieval efficiency and relevance, suggesting a path to modernize digital library services.
Outdated library catalogs get a 21st-century upgrade with AI-powered semantic search and knowledge graph visualization, making research discovery faster and more intuitive.
Traditional Online Public Access Catalogues (OPACs) are becoming less effective due to the rapid growth of scholarly literature. Conventional search methods, such as keyword indexing and Boolean queries, often fail to support efficient knowledge discovery. This paper proposes a Smart OPAC framework that transforms traditional OPACs into intelligent discovery systems using artificial intelligence and knowledge graph techniques. The framework enables semantic search, thematic filtering, and knowledge graph-based visualization to enhance user interaction and exploration. It integrates multiple open scholarly data sources and applies semantic embeddings to improve relevance and contextual understanding. The system supports exploratory search, semantic navigation, and refined result filtering based on user-defined themes. Quantitative evaluation demonstrates improvements in retrieval efficiency, relevance, and reduction of information overload. The proposed approach offers practical implications for modernizing digital library services and supports next-generation research workflows. Future work includes user-centric evaluation, personalization, and dynamic knowledge graph updates.