构建博物馆数据知识图谱，支持自然语言查询与关系探索。
MUSEKG: A Knowledge Graph Over Museum Collections
- 将文物、人物、图像等异构数据组织为带类型的图结构。
- 支持自然语言提问，返回可追溯的证据邻域结果。
- 适合文化机构研究者和数字策展人使用。
文化遗产数字化产生了大量但分散的博物馆藏品数据，涵盖结构化目录记录、图像及非结构化描述。现有博物馆信息系统难以将这些资源整合为统一、可查询的关系感知表示。我们提出MuseKG，一个交互式知识图谱系统，将对象、人物、组织、图像、图像标签及提取的语义实体在一致的模式下连接成有类型图结构。MuseKG通过将用户问题锚定到图实体，检索紧凑的证据邻域以生成答案，支持自然语言查询。在真实博物馆藏品上的交互演示表明，该系统能有效支持属性查找、关系探索与关系感知检索任务，且答案可通过显式的图结构进行验证。
原文摘要 · Abstract (English)
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.
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