Can this robot query my Linked Data Store? Exploring retrieval-augmented models for Repository Search & Discovery

The University of Toronto Scarborough Library's Digital Scholarship Unit has been developing multiformat and multilingual digital collections for over ten years, with a focus on post-custodial and non-extractive models. This presentation explores the integration of linked data and AI-powered chat-based search, using one specific project as a use case. The Dragomans project, which explores the role of diplomatic interpreter-translators in the Ottoman Empire, provides a multilingual dataset with linked data that allows us to assess the effectiveness of various retrieval-augmented generation methods when implementing chat-based search. This research offers insights that are applicable across various knowledge domains. The limitations of generative artificial intelligence in handling structured data, complex reasoning, and contextual understanding are well described. We compare various retrieval-augmented models to direct SPARQL/CIPHER queries and compare the results. This allows us to evaluate these approaches and comment on their effectiveness as well as resource implications. Our findings uniquely illuminate the landscape of generative AI, providing valuable guidance for future repository infrastructure development and contributing to the broader discourse on the integration of AI and linked data in digital repositories.