VIPR: A Visual Interface Tool for Programming Semantic Web Rules

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Semantic technologies have evolved from the initial purpose of supporting semantic integration, information exchange for the semantic web, towards a generic set of engineering tools for knowledge modelling, representation, inference. However, there is still much work required within the area of Semantic computing, the area highlights a key research challenge involving the complexity in engineering Semantic rules, associated dedicated models. Many existing tools focus on the creation of models, but concentrate on providing support for domain experts, isolating users with no knowledge engineering experience. This paper aims to address this issue by introducing a novel approach to enable the visual creation of Semantic web rules, for use within ontological models, context-aware applications. The developed tool, known as VIPR, aims to provide a user-friendly, interactive approach to aid in the creation of Semantic rules for ontologies. The work describes the design process involved in creating VIPR, presents the results of a comparative user evaluation. The research highlights the extent to which this tool has on improving the usability, intuitiveness of creating rules in an interactive environment, assesses how the tool can improve the learnability level for users with no prior knowledge engineering experience

Mining usage data for adaptive personalisation of smartphone based help-on-demand services

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile computing devices and their applications that encompass context aware components are becoming increasingly more prevalent. The context-awareness of these types of applications typically focuses on the services offered. In this paper we describe a framework that supports the monitoring and analysis of mobile application usage patterns with the goal of updating user models for adaptive services and user interface personalisation. This paper focuses on two aspects of the framework. The first is the modelling and storage of the usage data. The second focuses on the data mining component of the framework, outlining the five different capabilities of the adaptation in addition to the algorithms used. The proposed framework has been evaluated through specific case studies, with the results attained demonstrating the effectiveness of the data mining capabilities and in particular the adaptation of the User Interface. The accuracy and efficiency of the algorithms used are also evaluated with three users. The results of the evaluation show that the aims of the data mining component were achieved with the personalisation and adaptation of content and user interface, respectively

Learning Behaviour for Service Personalisation and Adaptation

Context-aware applications within pervasive environments are increasingly being developed as services and deployed in the cloud. As such these services are increasingly required to be adaptive to individual users to meet their specific needs or to reflect the changes of their behavior. To address this emerging challenge this paper introduces a service-oriented personalisation framework for service personalisation with special emphasis being placed on behavior learning for user model and service function adaptation. The paper describes the system architecture and the underlying methods and technologies including modelling and reasoning, behavior analysis and a personalisation mechanism. The approach has been implemented in a service-oriented prototype system, and evaluated in a typical scenario of providing personalised travel assistance for the elderly using the help-on-demand services deployed on smartphone

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

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.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

A User Profile Ontology Based Approach for Assisting People with Dementia in Mobile Environments

Personalization and context-aware applications have attracted increasing amounts of attention over recent years due to the emergence of pervasive computing applications. Nevertheless, it still remains a challenge to meet the needs of users while they are on the move. This paper introduces a novel approach for providing personalized, context-aware assistance services for users in mobile environments. Central to the approach is the use of ontological user profile modeling which captures various characteristics of a user in order to create a unique set of profile information. In addition, user profiles can adapt to changing user behavior, thus enabling services to respond to evolving user needs and preferences. We describe the overall system architecture of the proposed approach with special emphasis being placed on the user profile modelling and its expected utility based on a typical use case scenario, i.e., using a smart-phone to address the problem of the outdoor mobility of a person with Dementia. A prototype based on the Android OS is used to illustrate the application. The use of everyday technology for a real world problem highlights the potential and utility of our approach

Using Ontologies for Managing User Profiles in Personalised Mobile Service Delivery, Health Monitoring and Personalized Feedback using Multimedia Data

We are now living in a technological world where the adoption of pervasive technologies is becoming more prevalent. This has sparked growth in the development of services for delivery in pervasive environments, across a number of application domains including healthcare. User personalisation, in particular, has become an important element for delivering pervasive healthcare, which has coincided with the rapid increase in the use of smart-phone technologies. Increased user dependence on technology has resulted in a need to provide personalised service delivery, in the form of adaptive technology. Many studies have explored the use of ontological user modelling techniques to facilitate mobile service personalisation. Ontological user models have been developed for use within personalised web information retrieval systems, adaptive user interface design and within public services. Nevertheless, these models have not been adopted to implement the personalisation of assistive services for mobile users within pervasive environments. Every person is unique and therefore, will exhibit unique behaviours, wants and needs, which will also change over time. Adaptive technologies must be able to cater for human behavioural changes, and change to suit them via on-demand service delivery. This Chapter focuses on two key perspectives. Firstly, the modelling of different users within pervasive environments is introduced and critiqued and secondly, the topics of ontological modelling and user profile representation are contextualised within a discussion surrounding previous research undertaken by the authors

Using SWRL and ontological reasoning for the personalization of context-aware assistive services

The prevalence and advancements of existing context-aware applications are limited in their support of personalization for the user. The increase in the use of context-aware technologies has sparked growth in assistive applications and there is now a need to enable the adaptation of such technologies to reflect the changes in user behaviors. This paper describes the conceptualization and development of a personalization mechanism that can be integrated into a context-aware application for the purposes of providing an adaptable, mobile-based service to a user. We highlight the use of an ontological User Profile Model to provide a detailed representation of a user for use within adaptive applications. Special emphasis is placed on the use of rule-based reasoning using the Semantic Web Rule Language (SWRL). The paper details how these rules are created and used alongside the User Profile for the purposes of application personalization. We present a case study to illustrate the use of SWRL within the User Profile Model. Specifically, the case study focuses on providing personalized travel assistance to older users, with the use of self-service ticket machines via an `on-demand' context-aware smart-phone

Ontological User Profile Modelling for Personalization of Context-Aware Applications

Existing context-aware adaptation techniques are limited in their support for user personalization. There is relatively less developed research involving adaptive user modeling for user applications in the emerging areas of mobile and pervasive computing. This paper describes the creation of a User Profile Ontology for context-aware application personalization within mobile environments. We analyze users’ behavior and characterize users’ needs for context-aware applications. Special emphasis is placed in the ontological modeling of dynamic components for use in adaptable applications. We illustrate the use of the model in the context of a case study, focusing on providing personalized services to older people via smart-device technologies