Abstract. Museums around the world have built databases with metadata about millions of objects, their history, the people who created them, and the entities they represent. This data is stored in proprietary databases and is not readily available for use. Recently, museums embraced the Semantic Web as a means to make this data available to the world, but the experience so far shows that publishing museum data to the linked data cloud is difficult: the databases are large and complex, the information is richly structured and varies from museum to museum, and it is difficult to link the data to other datasets. This paper describes the process and lessons learned in publishing the data from the Smithsonian American Art Museum (SAAM). We highlight complexities of the database-to-RDF mapping process, discuss our experience linking the SAAM dataset to hub datasets such as DBpedia and the Getty Vocabularies, and present our experience in allowing SAAM personnel to review the information to verify that it meets the high standards of the Smithsonian. Using our tools, we helped SAAM publish high-quality linked data of their complete holdings (41,000 objects and 8,000 artists).
For example, the Smithsonian American Art Museum 1 used KARMA REF to publish their data as linked data, a tool trying to automate the mapping process and allowing the users to adjust the generated mappings.
G.: Connecting the Smithsonian American Art Museum to the Linked Data Cloud
In: Proceedings of the 10th Extended Semantic Web Conference (ESWC) (2013
Computer Science