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.

Ice cores in freezers, dinosaurs on display, fish in jars, birds in boxes, human remains and ancient artifacts from long gone civilizations that few people ever see – museum collections are filled with all this and more. These collections are treasure troves that recount the planet's natural and human history, and they help scientists in a variety of different fields such as geology, paleontology, anthropology and more. What you see on a trip to a museum is only a sliver of the wonders held in their collection. Museums generally want to make the contents of their collections available for teachers and researchers, either physically or digitally. However, each collection's staff has its own way of organizing data, so navigating these collections can prove challenging.

To study social, economic, and historical questions, researchers in the social sciences and humanities have started to use increasingly large unstructured textual datasets. While recent advances in NLP provide many tools to efficiently process such data, most existing approaches rely on generic solutions whose performance and suitability for domain-specific tasks is not well understood. This work presents an attempt to bridge this domain gap by exploring the use of modern Entity Linking approaches for the enrichment of museum collection data. We collect a dataset comprising of more than 1700 texts annotated with 7,510 mention-entity pairs, evaluate some off-the-shelf solutions in detail using this dataset and finally fine-tune a recent end-to-end EL model on this data. We show that our fine-tuned model significantly outperforms other approaches currently available in this domain and present a proof-of-concept use case of this model. We release our dataset and our best model.

Two centuries after Charles Darwin put the theory forward, a new study finally shows that birds living near the equator are more colourful. Scientists have used artificial intelligence (AI) to identify the amount of colour in photos of over 24,000 preserved birds from the Natural History Museum's collection. Tropical birds living near the equator are roughly 30 per cent more colourful than non-tropical birds living nearer the poles, the scientists found, but they don't know exactly why. The long-held theory, first suspected by Charles Darwin and other naturalists in the 18th and 19th centuries, hasn't been proven until now, the experts say. Research from the University of Sheffield found tropical birds living near the equator are roughly 30 per cent more colourful than non-tropical birds living nearer the poles.