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Linking and clustering artworks using social tags: Revitalizing crowd-sourced information on cultural collections

Title
Linking and clustering artworks using social tags: Revitalizing crowd-sourced information on cultural collections
Authors
Chae, GunhoPark, JaramPark, JuyongYeo, Woon SeungShi, Chungkon
Ewha Authors
여운승
SCOPUS Author ID
여운승scopusscopus
Issue Date
2016
Journal Title
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
ISSN
2330-1635JCR Link2330-1643JCR Link
Citation
vol. 67, no. 4, pp. 885 - 899
Keywords
museum informaticssimilarityautomatic classification
Publisher
WILEY-BLACKWELL
Indexed
SCIE; SSCI; SCOPUS WOS scopus
Abstract
Social tagging is one of the most popular methods for collecting crowd-sourced information in galleries, libraries, archives, and museums (GLAMs). However, when the number of social tags grows rapidly, using them becomes problematic and, as a result, they are often left as simply big data that cannot be used for practical purposes. To revitalize the use of this crowd-sourced information, we propose using social tags to link and cluster artworks based on an experimental study using an online collection at the Gyeonggi Museum of Modern Art (GMoMA). We view social tagging as a folksonomy, where artworks are classified by keywords of the crowd's various interpretations and one artwork can belong to several different categories simultaneously. To leverage this strength of social tags, we used a clustering method called link communities to detect overlapping communities in a network of artworks constructed by computing similarities between all artwork pairs. We used this framework to identify semantic relationships and clusters of similar artworks. By comparing the clustering results with curators' manual classification results, we demonstrated the potential of social tagging data for automatically clustering artworks in a way that reflects the dynamic perspectives of crowds.
DOI
10.1002/asi.23442
Appears in Collections:
신산업융합대학 > 융합콘텐츠학과 > Journal papers
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