Learning a taxonomy from a set of text documents
Applied Soft Computing
Constructing the virtual Jing-Hang Grand Canal with onto-draw
Expert Systems with Applications: An International Journal
Domain specific data retrieval on the semantic web
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
A folksonomy-based recommender system for personalized access to digital artworks
Journal on Computing and Cultural Heritage (JOCCH)
SMARTMUSEUM: A mobile recommender system for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
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The authors present a method for automatic annotation of objects in digital cultural heritage collections. Given a set of objects each accompanied by a text description, a set of structured vocabularies, a metadata schema, and a training set of annotations of the text descriptions, the method produces annotations for the objects. These annotations consist of structured vocabulary concepts or named entities (for example, Paris as a city) and metadata schema roles that each concept plays in an annotation (for example, Paris as a subject matter). The method focuses on identifying the metadata schema roles. The authors have evaluated the method using the ARIA collection from Rijksmuseum Amsterdam. The evaluation used four structured vocabularies, an artwork annotation schema, and a collection of natural language descriptions of artworks. The method achieved 61.2 percent accuracy in role identification, outperforming the baseline method without background knowledge (p