Transforming OPACs into Intelligent Discovery Systems: An AI-Powered, Knowledge Graph-Driven Smart OPAC for Digital Libraries

Researchers M. S. Rajeevan and B. Mini Devi have published a paper proposing a new framework to overhaul traditional Online Public Access Catalogues (OPACs). Dubbed the 'Smart OPAC,' the system aims to solve the growing ineffectiveness of conventional keyword and Boolean search methods in the face of exploding scholarly literature. The core innovation is the integration of artificial intelligence and knowledge graph techniques, which together transform a simple catalog into an intelligent discovery platform.

The proposed framework enables three key advanced functionalities: semantic search for understanding context and meaning, thematic filtering to narrow results by user-defined topics, and knowledge graph-based visualization to explore connections between works. It achieves this by integrating multiple open scholarly data sources and applying semantic embeddings—AI models that map text to numerical vectors—to drastically improve relevance and contextual understanding in search results.

A quantitative evaluation of the system demonstrates measurable improvements in retrieval efficiency and relevance, alongside a reduction in information overload for users. The authors position this as a practical solution for modernizing digital library services and supporting next-generation research workflows that require exploratory discovery rather than simple lookups. Future development paths include user-centric evaluation, personalization features, and implementing dynamic updates to the underlying knowledge graph.