Discovering, Visualizing and Sharing Knowledge through Personalized Learning Knowledge Maps
Jasminko Wurst, Michael Schneider, Martin Fleischmann, Monika Strauss · 2004
University of Dortmund,Artificial Intelligence Dept.D-44221 Dortmund, [email protected]. Thispaperpresentsan agent-based approach tosemanticex-ploration and knowledge discovery in large information spaces by meansofcapturing,visualizingandmakingusableimplicit knowledgestructuresof a group of users. The focus is on the developed conceptual model andsystem for creation and collaborative use of personalized learning knowl-edge maps. We use the paradigm of agents on the one hand as model forour approach, on the other hand it serves as a basis for an efficient imple-mentation of the system. We present an unobtrusive model for profilingpersonalised user agents based on two dimensional semantic maps thatprovide 1) a medium of implicit communication between human usersand the agents, 2) form of visual representation of resulting knowledgestructures. Concerning the issues of implementation we present an agentarchitecture, consisting of two sets of asynchronously operating agents,which enables both sophisticated processing, as well as short respondtimes necessary for enabling interactive use in real-time.