(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 12, 2011 120 | Page www.ijacsa.thesai.org Using Semantic Web to support Advanced Web- Based Environment Khaled M. Fouad Computer Science Dep., Community College, Taif Univ., Kingdom of Saudi Arabia (KSA) Mostafa A. Nofal Computer Engineering Dep., College of Computer Science and Information Systems, Taif Univ., Kingdom of Saudi Arabia (KSA). Hany M. Harb Computers and Systems Engineering Dept., Faculty of Eng., Al-Azhar Univ., Egypt. Nagdy M. Nagdy Engineering Applications and Computer Systems, Al-Baha Private College of Science, Kingdom of Saudi Arabia (KSA) Abstract—In the learning environments, users would be helpless without the assistance of powerful searching and browsing tools to find their way. Web-based e-learning systems are normally used by a wide variety of learners with different skills, background, preferences, and learning styles. In this paper, we perform the personalized semantic search and recommendation of learning contents on the learning Web- based environments to enhance the learning environment. Semantic and personalized search of learning content is based on a comparison of the learner profile that is based on learning style, and the learning objects metadata. This approach needs to present both the learner profile and the learning object description as certain data structures. Personalized recommendation of learning objects uses an approach to determine a more suitable relationship between learning objects and learning profiles. Thus, it may advise a learner with most suitable learning objects. Semantic learning objects search is based on the query expansion of the user query and by using the semantic similarity to retrieve semantic matched learning objects. Keywords- Semantic Web; Domain Ontology; Learner Profile; Adaptive Learning; Semantic Search ; Recommendation. I. INTRODUCTION Learning environment allows learners to access electronic course contents through the network and study them in virtual classrooms. It brings many benefits in comparison with conventional learning paradigm, e.g. learning can be taken at any time and at any place. However, with the rapid increase of learning content on the Web, it will be time-consuming for learners to find contents they really want to and need to study. The challenge in an information-rich world is not only to make information available to people at any time, at any place, and in any form, but to offer the right thing to the right person in the right way [1]. In the context of e-learning [2], adaptive systems are more specialized and focus on the adaptation of learning content and the presentation of this content. According to [3], an adaptive system focuses on how the profile data is learned by the learner and pays attention to learning activities, cognitive structures and the context of the learning material. In Figure 1, the structure of an adaptive system [5] is shown. The system intervenes at three stages during the process of adaptation. It controls the process of collecting data about the user, the process of building up the user model (user modeling) and during the adaptation process. Figure 1: The Structure of an Adaptive System [5] An advanced e-learning system has to comply with the following requirements [6]: Personalization: This requirement suggests that the learning process needs to take into account the user’s preferences and personal needs. This implies either that the user is in a position to specify explicitly these preferences or that the system has the ability to infer them through a monitoring process. The latter is far more convenient for the end-user and constitutes a highly desirable feature. Adaptivity: The user’s preferences change over time and the system must be able to track them and properly adjust to them. By ‘properly’, it is implied that the whole history of the user’s learning behavior must be taken into consideration, and not just the user’s latest (most recent) actions. Extensibility: An e-learning system has to be extensible in terms of the learning material it provides. The incorporation of new courses and resources must be an easy to accomplish the task. Data about User User Model Syste m User Modeling Adaptation Adaptation Effect