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After the emergence of Web 2.0, online art museums have been evolving into participatory museums, in an attempt to increase the public’s participation through the utilization of social media. Among many types of social media, social tagging has been receiving widespread attention as a tool for reducing the semantic gap between curators and visitors, through the group knowledge obtained from the active participation of the public.
In this circumstance, Gyeonggi Museum of Modern Art (GMOMA) embarked on an ongoing project with us to explore the potential of social tagging and applying it into museum management strategy. In the end of 2009, we built our own tag database based on the collections from GMOMA, and experiments were carried out by building a testbed on a website that was created to collect tags of 128 pieces of artworks.
After collecting the tags, we evaluated the feasibility of social tagging systems through workshops with curators from GMOMA. From the workshop we found the potentials of social tagging systems in museums through interviews and discussions with the curators, and identified the improvements that could be made in order to apply it to actual museums.
However, we discovered that while the number of tags increased, social tagging systems showed limitations in providing meaningful information and supporting semantic relationships between tags and museum collections.
The causes are as follows:
Lack of order, structure and depth in tags
Free forms of tags can cause ambiguity, chaos and noise
Spam tagsFailure to show the semantic relationships between tags; only provides an alphabetical list
Thus to achieve a participatory web and reflect the visitors’ semantic appreciation of museum collections, we conclude that the existing tagging systems should be supplemented. To improve the existing social tagging system and enhance the semantic appreciation in online art museum, our suggested solution is faceted tagging system which gives a guideline or schema to users when tagging the individual artworks. By collecting tags through the faceted tagging systems, we can automatically obtain a semantic structure and meaningful groups of tags. Before implementing the faceted tagging system and proving that it works, we had to make facets that cover the all the categories of art museum tags. We proceeded with card-sorting tests to extract and verify facets from the collected tag database. We retrieved six facets – “Background, Identification, Theme, Association, Emotion and Figure” – based on the semantic structure of tags, which were in a mess but now can be categorized into meaningful groups (facets).
Finally, user-tests are scheduled in order to prove that applying the six facets into the faceted tagging system can help to bridge the semantic gap between curators and audiences. For the user-tests, the same 128 artworks from the first experiment will be used, and we will compare the tags collected from the user-tests with the tags from the first experiment. Then we plan to discuss the feasibility of faceted tagging systems and its results – which we call structured tags – through a workshop with the curators from GMOMA.