Towards automated knowledge-based mapping between individual conceptualisations to empower personalisation of Geospatial Semantic Web

Geospatial domain is characterised by vagueness, especially in the semantic disambiguation of the concepts in the domain, which makes defining universally accepted geo- ontology an onerous task. This is compounded by the lack of appropriate methods and techniques where the individual semantic conceptualisations can be captured and compared to each other. With multiple user conceptualisations, efforts towards a reliable Geospatial Semantic Web, therefore, require personalisation where user diversity can be incorporated. The work presented in this paper is part of our ongoing research on applying commonsense reasoning to elicit and maintain models that represent users' conceptualisations. Such user models will enable taking into account the users' perspective of the real world and will empower personalisation algorithms for the Semantic Web. Intelligent information processing over the Semantic Web can be achieved if different conceptualisations can be integrated in a semantic environment and mismatches between different conceptualisations can be outlined. In this paper, a formal approach for detecting mismatches between a user's and an expert's conceptual model is outlined. The formalisation is used as the basis to develop algorithms to compare models defined in OWL. The algorithms are illustrated in a geographical domain using concepts from the SPACE ontology developed as part of the SWEET suite of ontologies for the Semantic Web by NASA, and are evaluated by comparing test cases of possible user misconceptions

The Partial Evaluation Approach to Information Personalization

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio

A Framework for Exploiting Internet of Things for Context-Aware Trust-based Personalized Services

In the last years, we have witnessed the introduction of Internet of Things as an integral part of the Internet with billions of interconnected and addressable everyday objects. On the one hand, these objects generate massive volume of data that can be exploited to gain useful insights into our day-to-day needs. On the other hand, context-aware recommender systems (CARSs) are intelligent systems that assist users to make service consumption choices that satisfy their preferences based on their contextual situations. However, one of the major challenges in developing CARSs is the lack of functionality providing dynamic and reliable context information required by the recommendation decision process based on the objects that users interact with in their environments. Thus, contextual information obtained from IoT objects and other sources can be exploited to build CARSs that satisfy users’ preferences, improve quality of experience and recommendation accuracy. This article describes various components of a conceptual IoT based framework for context-aware personalized recommendations. The framework addresses the weakness whereby CARSs rely on static and limited contextual information from user’s mobile phone, by providing additional components for reliable and dynamic contextual information, using IoT context sources. The core of the framework consists of context recognition and reasoning management, dynamic user profile model incorporating trust to improve accuracy of context-aware personalized recommendations. Experimental evaluations show that incorporating context and trust in personalized recommendations can improve its accuracy

Personalized Web Search Techniques - A Review

Searching is one of the commonly used task on the Internet. Search engines are the basic tool of the internet, from which related information can be collected according to the specified query or keyword given by the user, and are extremely popular for recurrently used sites. With the remarkable development of the World Wide Web (WWW), the information search has grown to be a major business segment of a global, competitive and money-making market. A perfect search engine is the one which should travel through all the web pages inthe WWW and should list the related information based on the given user keyword. In spite of the recent developments on web search technologies, there are still many conditions in which search engine users obtains the non-relevant search results from the search engines. A personalized Web search has various levels of efficiency for different users, queries, and search contexts. Even though personalized search has been a major research area for many years and many personalization approaches have been examined, it is still uncertain whether personalization is always significant on different queries for diverse users and under different search contexts. This paper focusses on the survey of many efficient personalized Web search approaches which were proposed by many authors

Context-driven progressive enhancement of mobile web applications: a multicriteria decision-making approach

Personal computing has become all about mobile and embedded devices. As a result, the adoption rate of smartphones is rapidly increasing and this trend has set a need for mobile applications to be available at anytime, anywhere and on any device. Despite the obvious advantages of such immersive mobile applications, software developers are increasingly facing the challenges related to device fragmentation. Current application development solutions are insufficiently prepared for handling the enormous variety of software platforms and hardware characteristics covering the mobile eco-system. As a result, maintaining a viable balance between development costs and market coverage has turned out to be a challenging issue when developing mobile applications. This article proposes a context-aware software platform for the development and delivery of self-adaptive mobile applications over the Web. An adaptive application composition approach is introduced, capable of autonomously bypassing context-related fragmentation issues. This goal is achieved by incorporating and validating the concept of fine-grained progressive application enhancements based on a multicriteria decision-making strategy

A Semantic Social Recommender System Using Ontologies Based Approach For Tunisian Tourism

Tunisia is well placed in terms of medical tourism and has highly qualified and specialized medical and surgical teams. Integrating social networks in Tunisian medical tourism recommender systems can result in much more accurate recommendations. That is to say, information, interests, and recommendations retrieved from social networks can improve the prediction accuracy. This paper aims to improve traditional recommender systems by incorporating information in social network; including user preferences and influences from social friends. Accordingly, a user interest ontology is developed to make personalized recommendations out of such information. In this paper, we present a semantic social recommender system employing a user interest ontology and a Tunisian Medical Tourism ontology. Our system can improve the quality of recommendation for Tunisian tourism domain. Finally, our social recommendation algorithm is implemented in order to be used in a Tunisia tourism Website to assist users interested in visiting Tunisia for medical purposes

Learner course recommendation in e-learning based on swarm intelligence

Se dan unas recomendaciones en la enseñanza asistida por ordenador (e-learning) basada en la inteligencia colectiva.This paper analyses aspects about the recommendation process in distributedinformation systems. It extracts similarities and differences between recommendations in estores and the recommendations applied to an e-learning environment. It also explains the phenomena of self-organization and cooperative emergence in complex systems coupled with bio-inspired algorithms to improve knowledge discovery and association rules. Finally, the present recommendation is applied to e-learning by proposing recommendation by emergence in a multi.agent system architecture