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Please use this identifier to cite or link to this item: http://hdl.handle.net/2262/863

Title: Adaptive Ontology-Driven Personalised News Services
Author: Tallon, Shane
Advisor: Conlan, Owen
Keywords: Computer Science
Issue Date: Sep-2005
Abstract: Ontologically Driven Adaptation is a challenging field within the area of Personalised News. Ontologies provide a structured, semantically rich methodology for the modelling of a domain. Personalised News is an area of Adaptive Hypermedia which is only beginning to take shape. There is a broad range of news that can be personalised, and a variety of ways in which a Personalised News System can be developed. Adaptive Hypermedia can be leveraged to provide a Personalised News Service for users who have interests in particular domains. Based on the user model, the domain, and the system itself, the news delivered can be high level general news, or low level detailed news, depending primarily on which type a user wishes to receive. This Thesis looks at the tangible differences between the results provided by two differently modelled domain models, which represent the same information space. One domain model is a structured view of the information space, and it contains rich semantic meaning. By this it is meant that it has rich semantic knowledge about the concepts residing within the domain, and relationships between those concepts. This domain model is expressed as an ontology and will be known from here on as a Strict Ontology. The second domain model, is a taxonomy of the information space with little or no semantic detail of the information space. It knows nothing of the relationships between objects other than the fact that they are related. This domain model will from here on be known as a Loose Ontology. This Thesis will evaluate the relative benefits of the two different approaches to representing an information space by examining the personalised user experiences offered. This will be carried out against the backdrop of an innovative approach to offering personalised ontologically-driven news.
URI: http://hdl.handle.net/2262/863
Appears in Collections:Computer Science (Theses and Dissertations)
Computer Science Technical Reports

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