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Adaptive Ontology-Driven Personalised News Services Adaptive Ontology-Driven Personalised News Services Shane Tallon A dissertation submitted to the University of Dublin, in partial fulfilment of the requirements for the degree of Master of Science in Computer Science 2005 Declaration I declare that the work described in this dissertation is, except where otherwise stated, entirely my own work and has not been submitted as an exercise for a degree at this or any other university. Signed: _____________________ Shane Tallon September 2005 I Permission to lend and/or copy I agree that Trinity College Library may lend or copy this dissertation upon request. Signed: _____________________ Shane Tallon September 2005 II ACKNOWLEDGEMENTS Firstly, I would like to thank my supervisor Dr. Owen Conlan, whose expert advice, insight and guidance proved invaluable in the writing of this Thesis. I would also like to thank Ian O’Keeffe whose technical expertise was of great help. Thank you also to the members of the Knowledge and Data Engineering Group in Trinity College, some of whom were guinea pigs for the personalised news service, and others who provided even more insight and advice. I would like to thank my Mam and Dad, Lorraine and Andy, for their constant love, encouragement and support through the year, and thanks to Dad for proof reading my terrible English. Thanks also go to my brother Barry, my two sisters, Aoibhínn and Elenore, and to my Grandparents for providing me with all of those lunches and dinners, and their prayers. I would like to thank the NDS class, for making the year a very enjoyable experience, and to my friends from home, who made sure I never missed a night out regardless of what work had to be done. Finally, I would like to dedicate this thesis to the memory of my good friend and Granddad, Dan. III 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. IV TABLE OF CONTENTS ACKNOWLEDGEMENTS ................................................................................................................ III ABSTRACT ......................................................................................................................................... IV TABLE OF CONTENTS ......................................................................................................................V TABLE OF FIGURES ..................................................................................................................... VIII 1 INTRODUCTION ...............................................................................................................................1 1.1 MOTIVATION ..................................................................................................................................1 1.2 RESEARCH QUESTION .....................................................................................................................2 1.3 OBJECTIVES AND GOALS ................................................................................................................2 1.4 CONTRIBUTION TO STATE OF THE ART ...........................................................................................3 1.5 OVERVIEW ......................................................................................................................................3 2 STATE OF THE ART.........................................................................................................................5 2.1 ADAPTIVE HYPERMEDIA .................................................................................................................5 2.2 SURVEY OF ADAPTIVE HYPERMEDIA SYSTEMS ..............................................................................7 2.3 ANALYSIS OF ADAPTIVE HYPERMEDIA SYSTEMS .........................................................................10 2.4 PERSONALISED E LEARNING ..........................................................................................................12 2.4.1 Adaptive Authoring ..............................................................................................................14 2.5 E LEARNING ANALYSIS ..................................................................................................................15 2.6 SURVEY OF PERSONALISED NEWS ................................................................................................15 2.7 ANALYSIS OF PERSONALISED NEWS .............................................................................................18 2.8 ARTIFICIAL INTELLIGENCE METHODS FOR PERSONALISED NEWS ................................................20 2.9 AI METHODS ANALYSIS ...............................................................................................................22 2.10 THE SEMANTIC WEB ...................................................................................................................22 2.11 ONTOLOGICALLY DRIVEN ADAPTATION .....................................................................................23 2.12 TECHNOLOGIES ...........................................................................................................................24 2.12.1 XML ...................................................................................................................................24 2.12.2 RSS.....................................................................................................................................24 2.12.3 RDF....................................................................................................................................24 2.12.4 Ontology Structure (OWL/DAML+OIL)............................................................................24 2.12.5 Protégé...............................................................................................................................25 2.12.6 JESS ...................................................................................................................................25 2.12.7 JATHA................................................................................................................................25 2.12.8 LISP ...................................................................................................................................26 2.13 SUMMARY ..................................................................................................................................26 3 DESIGN..............................................................................................................................................27 3.1 INTRODUCTION .............................................................................................................................27 V 3.2 INFLUENCES FROM STATE OF THE ART .........................................................................................27 3.2.1 Adaptive Axes.......................................................................................................................27 3.2.2 User Model...........................................................................................................................28 3.2.3 Ontologies............................................................................................................................30 3.2.4 Choice of System..................................................................................................................31 3.3 DOMAIN OF NEWS ........................................................................................................................32 3.3.1 Modelling of the Domain .....................................................................................................33 3.4 FRAMEWORK DESIGN ...................................................................................................................34
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