Abstract:

The recent technological developments in the area of social-networks and user generated content have already changed the learning behaviour of any learners. The exploration of knowledge through the world-wide-web using different content-provider, e.g. Wikipedia or IEEE Xplore plays a key role for researcher, students or knowledge workers. The usage of users’ knowledge or user generated content is a self-evident process within the working or learning workflow. Different approaches try to face the mass of information given in the WWW to support a more effective learning process, whereas semantic-technologies are a promising approach for organizing the knowledge. The formal knowledge descriptions like Ontologies used by Semantic Technologies only provide approaches to describe knowledge within a given and predefined knowledge-domain. The graphical representation of these domains provides another opportunity to explore the pre-engineered domain, whereas the learner with his individual learning aptitudes, learning behaviour and interests is not involved in the process of knowledge representation.
The following paper describes the conceptual design of an Intelligent Exploration System (IES) that offers a user-adapted graphical environment of user generated web-based knowledge repositories, to support and optimize the explorative learning on web. The paper starts with a related work study followed by the comparative study of existing content-providing systems based on user generated content. The conceptual part of the paper will show how existing recommendation systems and interaction-analysis algorithms could be used to optimize both the content and the visual appearance of explorative knowledge visualizations. This part further shows actual results of graphical knowledge representations. The paper will be concluded with a case study of a knowledge worker in a research institution using the Intelligent Exploration System for his research work.

Keywords:

adaptive learning systems, intelligent learning systems, its, intelligent tutoring systems, explorative learning, semantic knowledge visualizations.