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Automatic document-level semantic metadata annotation using folksonomies and domain ontologies

Automatic document-level semantic metadata annotation using folksonomies and domain ontologies
Automatic document-level semantic metadata annotation using folksonomies and domain ontologies
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 del.icio.us. 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 del.icio.us 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.
Al-Khalifa, Hend S.
b464bced-9859-47ef-829a-4675dfdba01e
Al-Khalifa, Hend S.
b464bced-9859-47ef-829a-4675dfdba01e

Al-Khalifa, Hend S. (2007) Automatic document-level semantic metadata annotation using folksonomies and domain ontologies. FACULTY OF ENGINEERING AND APPLIED SCIENCE, ELECTRONICS AND COMPUTER SCIENCE, Doctoral Thesis.

Record type: Thesis (Doctoral)

Abstract

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 del.icio.us. 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 del.icio.us 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.

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More information

Published date: June 2007
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 264181
URI: https://eprints.soton.ac.uk/id/eprint/264181
PURE UUID: 4d3ea933-c2f0-4e01-872c-977d2a546aee

Catalogue record

Date deposited: 21 Jun 2007
Last modified: 18 Jul 2017 07:39

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