arXiv:2511.16014v2 Announce Type: replace Abstract: Digitisation in the cultural heritage sector has produced large but fragmented repositories of museum collection data, spanning structured catalogue records, images, and unstructured descriptions. Existing museum information systems often make it difficult to integrate these sources into a unified, queryable representation that supports relation-aware exploration. We present MuseKG, an interactive knowledge graph system that organises heterogeneous museum data into a typed graph that links objects, people, organisations, images, image-derived
The proliferation of digitised cultural heritage data and advancements in AI-driven knowledge graph technologies enable more sophisticated organisation and interaction with museum collections.
This development allows for improved searchability, integration, and discovery of cultural heritage assets, making them more accessible for research, education, and public engagement.
Fragmented museum data can now be unified into queryable knowledge graphs, facilitating relation-aware exploration and potentially unlocking new insights from vast cultural repositories.
- · Museums & Cultural Institutions
- · AI/Knowledge Graph Developers
- · Researchers & Historians
- · Digital Archivists
- · Traditional Manual Curation Methods
- · Fragmented Data Silos
MuseKG directly integrates heterogeneous museum data into a unified, interactive knowledge graph.
This leads to enhanced public and academic access to cultural heritage, enabling cross-collection analyses and new research methodologies.
The broader adoption of such systems could create a globally interconnected digital cultural commons, fostering interdisciplinary innovation and cultural understanding.
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Read at arXiv cs.AI