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 labels, and extracted semantic entities within a coherent schema. MuseKG supports natural-language queries by grounding user questions to graph entities and retrieving a compact neighbourhood of evidence for answer generation. Through an interactive demonstration on real museum collections, we show that MuseKG supports common exploration tasks such as attribute lookup, relation exploration, and relation-aware retrieval, with answers that remain inspectable via explicit graph structures.

It has recently been discovered that using a pre-trained vision-language model (VLM), e.g., CLIP, to align a whole query image with several finer text descriptions generated by a large language model can significantly enhance zero-shot performance. However, in this paper, we empirically find that the finer descriptions tend to align more effectively with local areas of the query image rather than the whole image, and then we theoretically validate this finding. Thus, we present a method called weighted visual-text cross alignment (WCA). This method begins with a localized visual prompting technique, designed to identify local visual areas within the query image. The local visual areas are then cross-aligned with the finer descriptions by creating a similarity matrix using the pre-trained VLM. To determine how well a query image aligns with each category, we develop a score function based on the weighted similarities in this matrix. Extensive experiments demonstrate that our method significantly improves zero-shot performance across various datasets, achieving results that are even comparable to few-shot learning methods.