INTRODUCTION AND RELATED STUDIES
Taxonomy can be defined as a controlled vocabulary that establishes hierarchical or associative relationships between terms. One problem of taxonomy is that it depends too much on the human element; a library can acquire titles on any subject, but it is unreasonable to expect indexers to be experts in every field of knowledge (Steele, 2009). Another problem is a timeliness problem inherent in taxonomy; taxonomy needs to be agreed upon and codified into a classification prior to use by the indexers (Peters, 2009). This can lead to a situation where classification structures are not compatible with current knowledge.
Recently, users have started to use social software to store and tag documents with their own tags to make them retrievable. The collection of tags used within one platform is called folksonomy. Transforming the creation of explicit metadata for resources from a professional activity into a shared, communicative activity by users is an important development that should be explored and considered for future systems development (Mathes, 2004).
Folksonomy tags offer some advantages; they are easy to handle and take into account the users' own vocabulary. Yi (2009) attempted to assess the indexing value of social tags in a context of an information-retrieval model using the Latent Semantic Indexing method. His study result showed the potential of using social tags as indexing terms for the DDC-based classification of tagged resources. Further, Geisler and Burns (2007) stated that YouTube tags provide real added value especially for users searching, because 66% of them do not appear in the other metadata. Morrison (2008) compared search performance of folksonomies in information retrieval from social bookmarking sites with that of search engines and subject directories. The results of this study showed that search engines had the highest precision and recall rates; however, folksonomies fared surprisingly well.
Folksonomy tags suffer from some problems. The folksonomy-based approach lists tags without indicating relationships in flat name spaces, unlike the taxonomy-based approach, which displays words indicating relationships between them. Thus, folksonomies do not include any vocabulary control; synonyms are not bound together, and homonyms are not distinguished, which leads to a decrease in their retrieval effectiveness. The hype about folksonomies being a better method of information retrieval has by now given way to the realization that without a structure, they are not so powerful as previously assumed (Peters, 2009).
Many ideas are emerging on how to structure folksonomies with semantic information obtained from taxonomies, without sacrificing their features. Kolbitsch (2007) proposed WordFlickr, based on the use of WordNet for expanding Flickr queries. An informal experiment compared search results from the prototype implementation of WordFlickr with results from Flickr. However, this study did not formally verify that WordFlickr was superior to Flickr in terms of retrieval efficiency.
This study investigates the value of folksonomy tags for indexing videos and the feasibility of tag structure. Furthermore it also explores how effective is tag control through query expansion (tag gardening) in searching videos. To do so, we designed a structured folksonomy-based system (hereafter, a tagsonomy-based system) in which queries can be expanded through tag control; equivalent, synonymous, or related tags are bound together, in order to improve the retrieval effectiveness (recall and precision) of videos. Then, we evaluate the proposed system by comparing it to a tag-based system without tag control, in terms of recall and precision rates.