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Title: Automatic document-level semantic metadata annotation using folksonomies and domain ontologies
Author: Al-Khalifa, Hend S.
ISNI:       0000 0001 3406 5986
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2007
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The last few years have witnessed a fast growth of the concept of Social Software. Be it video sharing such as YouTube, photo sharing such as Flickr, community building such as MySpace, or social bookmarking such as These websites contain valuable user-generated metadata called folksonomies. Folksonomies are ad hoc, light-weight knowledge representation artefacts to describe web resources using people’s own vocabulary. The cheap metadata contained in such websites presents potential opportunities for us (researchers) to benefit from. This thesis presents a novel tool that uses folksonomies to automatically generate metadata with educational semantics in an attempt to provide semantic annotations to bookmarked web resources, and to help in making the vision of the Semantic Web a reality. The tool comprises two components: the tags normalisation process and the semantic annotation process. The tool uses the social bookmarking service as a source for folksonomy tags. The tool was applied to a case study consisting of a framework for evaluating the usefulness of the generated semantic metadata within the context of a particular eLearning application. This implementation of the tool was evaluated over three dimensions: the quality, the searchability and the representativeness of the generated semantic metadata. The results show that folksonomy tags were acceptable for creating semantic metadata. Moreover, folksonomy tags showed the power of aggregating people’s intelligence. The novel contribution of this work is the design of a tool that utilises folksonomy tags to automatically generate metadata with fine gained and extra educational semantics.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available