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This paper introduces CULTUREBOT, a framework leveraging knowledge graphs and retrieval-augmented generation to enable natural-language exploration of cultural heritage datasets. The study demonstrates that graph-guided retrieval significantly outperforms general-purpose assistants in evidence grounding, reasoning, and completeness when navigating complex relational structures within large-scale archives. This work advances the application of AI for specialized data discovery, moving beyond simple keyword search to structured, conversational access of domain-specific repositories.
CultureBot: A Framework for Retrieval-Augmented Exploration of Cultural Heritage Data
arXiv:2610.16435v1 Announce Type: new Abstract: Digital cultural heritage collections encode rich relations among objects, places, periods, providers, images, and source institutions, yet users typically access them through keyword search and faceted browsing. We present CULTUREBOT, an interactive framework for natural-language exploration of cultural heritage collections that combines knowledge-graph-guided retrieval, lexical-semantic retrieval, and source-linked generation. CULTUREBOT maps user queries to structured catalogue relations, enriches retrieved records with textual evidence, and generates answers that link back to the underlying repository records. We instantiate the framework on 81,852 SearchCulture records and release an online demonstration. In an expert evaluation involving six cultural-heritage specialists, 55 persona-grounded queries, and 250 evaluations, the graph-guided system was rated significantly higher than two general-purpose assistants and a non-graph ablation, with the largest gains in evidence grounding, diversity, reasoning, and completeness. These results support graph-guided retrieval as a practical way to expose the relational structure of cultural collections through conversational access while preserving links to catalogue evidence.
Abstract:Digital cultural heritage collections encode rich relations among objects, places, periods, providers, images, and source institutions, yet users typically access them through keyword search and faceted browsing. We present CULTUREBOT, an interactive framework for natural-language exploration of cultural heritage collections that combines knowledge-graph-guided retrieval, lexical-semantic retrieval, and source-linked generation. CULTUREBOT maps user queries to structured catalogue relations, enriches retrieved records with textual evidence, and generates answers that link back to the underlying repository records. We instantiate the framework on 81,852 SearchCulture records and release an online demonstration. In an expert evaluation involving six cultural-heritage specialists, 55 persona-grounded queries, and 250 evaluations, the graph-guided system was rated significantly higher than two general-purpose assistants and a non-graph ablation, with the largest gains in evidence grounding, diversity, reasoning, and completeness. These results support graph-guided retrieval as a practical way to expose the relational structure of cultural collections through conversational access while preserving links to catalogue evidence.
Computation and Language (cs.CL)
arXiv:2610.16435 [cs.CL]
(or arXiv:2610.16435v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.16435 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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