Semantically Driven Presentation of Context-Relevant Learning Material

Abstract. In this paper we present our work on a new approach for an intuitive presentation of semantic relationships for the results of a semantic search algorithm. The underlying semantic search retrieves the most relevant learning material according to the context of the user. It provides the user with qualified learning material which is intelligently retrieved based on the current working situation. To ease the understanding how the presented result list has been generated a new presentation form is introduced. The preliminary results of our work are shown in a prototype implementation used for radar image interpretation. The aim is to optimally assist the image interpreters by offering relevant help and learning material. Keywords: e-learning, semantic visualization, semantic retrieval, image interpretation 1. Introduction Specialists in complex working environments face the challenge to be always up-to-date with their knowledge. They have to keep up with the often rapid development in their field of work. Assistance systems can aid them in their working tasks whereas e-learning systems can offer task-related information and learning material. With a combination of assistance and e-learning systems employees are capable of adapting to new scenarios and challenges as well as to solve new problems and to refresh and update their knowledge. In some areas employees must continuously update their knowledge because of the transient character of their field of work. This renders the learning process as lifelong learning instead of a one-time learning at school or in a qualification program. Assistance systems and e-learning systems can help the user to deal with situations when experience and background knowledge are not sufficient to solve new and unexpected problems. In this paper we present an approach how to present context relevant information of an interlinked assistance and e-learning system. The application scenario is e-learning for image interpretation. The work of an image interpreter perfectly fits the description of a complex working environment. Image interpreters not only must be able recognise various complex objects but they also require background knowledge for the correct and sound interpretation of the images. Different sensor and imaging parameters, a high variety in appearance of objects around the globe and time pressure create a challenging and demanding working environment. One of the most demanding tasks is the analysis of complex facilities, such as airfields, harbours and industrial installations, because they require immense technical background knowledge and deep understanding of the processes in such facilities. An additional difficulty arises with the use of complex imaging sensors, for instance radar image sensors. One manifestation of such a radar sensor is the Synthetic Aperture Radar (SAR) sensor. SAR is an imaging technology based on reflections of microwave pulses emitted by a radar sensor. Due to the complex imaging geometry and the very different reflection properties of objects in the microwave band, special training and substantial experience are required in order to be able to identify objects in this kind of images.