KG-LibRAG: A Conceptual Framework for Knowledge Graph-Driven Generative AI in Intelligent Information Retrieval and Personalised Resource Recommendation in Academic Libraries

Academic library discovery has long depended on matching words, while the knowledge that libraries curate — about subjects, authors, citations and curricula — has remained largely unused at the moment of search. This paper has proposed KG-LibRAG, a conceptual framework that places that knowledge at the centre of generative AI-based discovery. It contributes a library-domain ontology; a hybrid architecture in which lexical, dense and graph retrieval are fused and generation is constrained to verifiable evidence; a personalised recommendation mechanism with path-based explanations; and five propositions with an evaluation protocol through which the framework can be tested against conventional retrieval. Its contribution is conceptual: the framework is offered as a basis for empirical work, and the protocol is designed to make that work comparable across institutions. The broader argument is that trustworthy AI in academic libraries is a sociotechnical achievement, which depends as much on metadata quality, librarian oversight, user consent and professional competence as on the capability of the models themselves.

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