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Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments

Skillen, Kerry-Louise, Chen, Liming, Nugent, Chris, Donnelly, Mark, Burns, William and Solheim, Ivar (2014) Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments. Future Generation Computer Systems, 34 . pp. 97-109. [Journal article]

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URL: http://dx.doi.org/10.1016/j.future.2013.10.027

DOI: doi:10.1016/j.future.2013.10.027

Abstract

Existing context-aware applications are limited in their support of user personalisation. Nevertheless, the increase in the use of context-aware technologies has sparked the growth in assistive applications resulting in a need to enable adaptation to reflect the changes in user behaviours. This paper introduces a systematic approach to service personalisation for mobile users in pervasive environments and presents a service-oriented distributed system architecture. The developed approach makes use of semantic technologies for user modelling and personalisation reasoning. In the paper we characterise user behaviours and needs in pervasive environments upon which ontological user models are created with special emphasis being placed on ontological modelling of dynamic and adaptive user profiles. We develop a rule-based personalisation mechanism that exploits semantic web rule mark-up language for rule design and a combination of semantic and rule-based reasoning for personalisation. We use two case studies focusing on providing personalised travel assistance for people using Help-on-Demand services deployed on a smart-phone to contextualise the discussions within the paper. The proposed approach is implemented in a prototype system, which includes Help-on-Demand services, content management services, user models and personalisation mechanisms in addition to application specific rules. Experiments have been designed and conducted to test and evaluate the approach with initial results demonstrating the functionality of the approach.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
ID Code:28965
Deposited By: Dr Mark Donnelly
Deposited On:27 Mar 2014 16:28
Last Modified:09 Mar 2017 16:24

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