MUSEKG: A Knowledge Graph Over Museum Collections

• URL: http://arxiv.org/abs/2511.16014v1
• Date: Thu, 20 Nov 2025 03:23:36 GMT
• Title: MUSEKG: A Knowledge Graph Over Museum Collections
• Authors: Jinhao Li, Jianzhong Qi, Soyeon Caren Han, Eun-Jung Holden,
• Abstract summary: MuseKG is an end-to-end knowledge-graph framework for museum information systems.<n>It unifies structured and unstructured museum data through symbolic-neural integration.
• Score: 19.587385754644256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital transformation in the cultural heritage sector has produced vast yet fragmented collections of artefact data. Existing frameworks for museum information systems struggle to integrate heterogeneous metadata, unstructured documents, and multimodal artefacts into a coherent and queryable form. We present MuseKG, an end-to-end knowledge-graph framework that unifies structured and unstructured museum data through symbolic-neural integration. MuseKG constructs a typed property graph linking objects, people, organisations, and visual or textual labels, and supports natural language queries. Evaluations on real museum collections demonstrate robust performance across queries over attributes, relations, and related entities, surpassing large-language-model zero-shot, few-shot and SPARQL prompt baselines. The results highlight the importance of symbolic grounding for interpretable and scalable cultural heritage reasoning, and pave the way for web-scale integration of digital heritage knowledge.

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