A semantic map approach to a navigation system for exploratory learning in hyperspace

We propose a framework based on a sub-symbolic approach for the support of exploratory activities in a hyperspace. By using it, it is possible to express the semantic features of the whole hyperspace and the states of exploratory activities in topological order. This approach is applied to generate navigation information for exploratory activity. The space explored changes automatically using the semantic similarities of the nodes which constitute that space. An extended self-organizing feature map is used as the semantic feature map of hyperspace, in short Hy-SOM. Hy-SOM is applied to express the user model and generate the navigation strategy for the user. The exploratory history of the user is first mapped on to Hy-SOM. Next, the semantic relations between nodes are shown on the map. The result reflects the exploratory state of the user interpreted with the help of a user model