MUSEKG: A Knowledge Graph Over Museum Collections

TL;DR

MuseKG is a comprehensive knowledge-graph framework that unifies heterogeneous museum data, enabling effective natural language querying and reasoning over cultural heritage collections, surpassing existing LLM baselines.

Contribution

The paper introduces MuseKG, a novel symbolic-neural integrated knowledge graph for museums, facilitating scalable, interpretable, and queryable digital heritage data.

Findings

MuseKG outperforms large-language-model baselines in query accuracy.

The framework effectively links diverse museum data types.

Symbolic grounding enhances interpretability and scalability.

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.