Folksonomies are social collaborative systems which represent a method of self- organisation, where users save their electronic resources online and create personal metadata (tags) to describe them. Users can share their resources with other users creating social networks between them. Recently folksonomies have spread widely and rapidly on the World Wide Web, and the number of web sites which employ folksonomies is increasing every day. The user's tags are freely chosen words which are not restricted to any controlled vocabulary rules. As a consequence they suffer from certain drawbacks (e.g. misspelling, synonymy and polysemy) which indicate that user-created tags, from the point of view of searching and usability, cannot be fully reliable. Many research projects have been carried out in the area of folksonomies and their usability, but there is little work to date focusing on searching within folksonomies. This thesis analyses the quality of user-created tags and examines how their quality can be enhanced for searching and sharing purposes. It makes a number of contributions to the field of folksonomies including: a suggested improvement to address the problem of limited quality and quantity of the user-created tags; and a prototype to enhance the user- created tags and overcome some of its limitation with automatically extracted tag sets which will lead to an improvement in search capability within folksonomy systems. Controlled experiments have been employed to determine the effectiveness of the prototype. The experiments were carried out to examine if the search results were more relevant to the user's query when using the enhanced tag set than the search results provided by the user-created tag set alone. The results of the experiment indicate that the relevancy of the search results were improved when using the enhanced tag set.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:550826 |
| Date | January 2011 |
| Creators | Awawdeh, Ruba |
| Publisher | University of Ulster |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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