Using concept similarity in cross ontology for adaptive e-Learning systems

Abstracte-Learning is one of the most preferred media of learning by the learners. The learners search the web to gather knowledge about a particular topic from the information in the repositories. Retrieval of relevant materials from a domain can be easily implemented if the information is organized and related in some way. Ontologies are a key concept that helps us to relate information for providing the more relevant lessons to the learner. This paper proposes an adaptive e-Learning system, which generates a user specific e-Learning content by comparing the concepts with more than one system using similarity measures. A cross ontology measure is defined, which consists of fuzzy domain ontology as the primary ontology and the domain expert’s ontology as the secondary ontology, for the comparison process. A personalized document is provided to the user with a user profile, which includes the data obtained from the processing of the proposed method under a User score, which is obtained through the user evaluation. The results of the proposed e-Learning system under the designed cross ontology similarity measure show a significant increase in performance and accuracy under different conditions. The assessment of the comparative analysis, showed the difference in performance of our proposed method over other methods. Based on the assessment results it is proved that the proposed approach is effective over other methods

Methods and techniques for generation and integration of Web ontology data

University of Technology, Sydney. Faculty of Information Technology.Data integration over the web or across organizations encounters several unfavorable features: heterogeneity, decentralization, incompleteness, and uncertainty, which prevent information from being fully utilized for advanced applications such as decision support services. The basic idea of ontology related approaches for data integration is to use one or more ontology schemas to interpret data from different sources. Several issues will come up when actually implementing the idea: (1) How to develop the domain ontology schema(s) used for the integration; (2) How to generate ontology data for domain ontology schema if the data are not in the right format and to create and manage ontology data in an appropriate way; (3) How to improve the quality of integrated ontology data by reducing duplications and increasing completeness and certainty. This thesis focuses on the above issues and develops a set of methods to tackle them. First, a key information mining method is developed to facilitate the development of interested domain ontology schemas. It effectively extracts from the web sites useful terms and identifies taxonomy information which is essential to ontology schema construction. A prototype system is developed to use this method to help create domain ontology schemas. Second, this study develops two complemented methods which are light weighted and more semantic web oriented to address the issue of ontology data generation. One method allows users to convert existing structured data (mostly XML data) to ontology data; another enables users to create new ontology data directly with ease.In addition, a web-based system is developed to allow users to manage the ontology data collaboratively and with customizable security constraints. Third, this study also proposes two methods to perform ontology data matching for the improvement of ontology data quality when an integration happens. One method uses the clustering approach. It makes use of the relational nature of the ontology data and captures different situations of matching, therefore resulting in an improvement of performance compared with the traditional canopy clustering method. The other method goes further by using a learning mechanism to make the matching more adaptive. New features are developed for training matching classifier by exploring particular characteristics of ontology data. This method also achieves better performance than those with only ordinary features. These matching methods can be used to improve data quality in a peer-to-peer framework which is proposed to integrate available ontology data from different peers

An Application of Collaborative Web Browsing Based on Ontology Learning from User Activities on the Web

With explosively increasing amount of information on the Web, users have been getting more bored to seek relevant information. Several studies have introduced adaptive approaches to recognizing personal interests. This paper proposes the collaborative Web browsing system that can support users to share knowledge with other users. Especially, we have focused on user interests extracted from their own activities related to bookmarks. A simple URL based bookmark is provided with semantic and structural information by the conceptualization based on ontology. In order to deal with the dynamic usage of bookmarks, ontology learning based on a hierarchical clustering method can be exploited. As a result of our experiments, about 53.1 % of the total time was saved during collaborative browsing for seeking the equivalent set of information, compared with single Web browsing. Finally, we demonstrate implementing an application of collaborative browsing system through sharing bookmark-associated activities

A user profiling component with the aid of user ontologies

Abstract: What follows is a contribution to the field of user modeling for adaptive teaching and learning programs especially in the medical field. The paper outlines existing approaches to the problem of extracting user information in a form that can be exploited by adaptive software. We focus initially on the so-called stereotyping method, which allocates users into classes adaptively, reflecting characteristics such as physical data, social background, and computer experience. The user classifications of the stereotyping method are however ad hoc and unprincipled, and they can be exploited by the adaptive system only after a large number of trials by various kinds of users. We argue that the remedy is to create a database of user ontologies from which readymade taxonomies can be derived in such a way as to enable associated software to support a variety of different types of users

Context-adaptive learning designs by using semantic web services

IMS Learning Design (IMS-LD) is a promising technology aimed at supporting learning processes. IMS-LD packages contain the learning process metadata as well as the learning resources. However, the allocation of resources - whether data or services - within the learning design is done manually at design-time on the basis of the subjective appraisals of a learning designer. Since the actual learning context is known at runtime only, IMS-LD applications cannot adapt to a specific context or learner. Therefore, the reusability is limited and high development costs have to be taken into account to support a variety of contexts. To overcome these issues, we propose a highly dynamic approach based on Semantic Web Services (SWS) technology. Our aim is moving from the current data- and metadata-based to a context-adaptive service-orientated paradigm We introduce semantic descriptions of a learning process in terms of user objectives (learning goals) to abstract from any specific metadata standards and used learning resources. At runtime, learning goals are accomplished by automatically selecting and invoking the services that fit the actual user needs and process contexts. As a result, we obtain a dynamic adaptation to different contexts at runtime. Semantic mappings from our standard-independent process models will enable the automatic development of versatile, reusable IMS-LD applications as well as the reusability across multiple metadata standards. To illustrate our approach, we describe a prototype application based on our principles

Requirements for an Adaptive Multimedia Presentation System with Contextual Supplemental Support Media

Investigations into the requirements for a practical adaptive multimedia presentation system have led the writers to propose the use of a video segmentation process that provides contextual supplementary updates produced by users. Supplements consisting of tailored segments are dynamically inserted into previously stored material in response to questions from users. A proposal for the use of this technique is presented in the context of personalisation within a Virtual Learning Environment. During the investigation, a brief survey of advanced adaptive approaches revealed that adaptation may be enhanced by use of manually generated metadata, automated or semi-automated use of metadata by stored context dependent ontology hierarchies that describe the semantics of the learning domain. The use of neural networks or fuzzy logic filtering is a technique for future investigation. A prototype demonstrator is under construction

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201