A Knowledge based Navigation System with a Semantic Map Approach

In this paper, we propose a framework based on a sub-symbolic approach for the support of exploratory activities (e.g. learning, training and education) in a hyperspace. By using our framework, it is possible to extract the knowledge on the semantic features of the whole hyperspace and the states of exploratory activities and structure in topological order. This approach is applied to generate the navigation information for the exploratory activity. The space explored is changed automatically by using the knowledge on semantic similarities of the nodes which constitute that space. An extended self-organizing feature map is used as the semantic feature map of the 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 firstly mapped on the 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. In this paper, we propose a framework based on a sub-symbolic approach for the support of exploratory activities/learning in a hyperspace. By using our framework, it is possible to express the semantic feature of whole hyperspace and the state of the exploratory activity in topological order. This approach is applied to generate the navigation information for exploratory activity. By using our approach, the space explored is reconstituted automatically by the semantic similarities of the nodes which constitute that space. This shapes a kind of map. We call this map the semantic features map of hyper space, in short Hy-SOM. The Hy-SOM is applied to express the user model and navigation strategy for user. The exploratory history of the users is mapped on the Hy-SOM. Then the semantic relations between nodes are given on the map. The result shows the exploratory states of the user. This is interpreted as a user model. The exploratory tendencies of the user are inferred from the above user's exploratory model. Based on the deduction of the user's exploratory tendencies and expert knowledge embedded in the system, a navigation trying to improve the undesirable patterns is developed. The navigation strategy parameters are instantiated by using the Hy-SOM, the hypermedia node hierarchy and the semantic attributes of both nodes and links. The result of the instantiation is an ordered list of nodes recommended by the system for reference in the next step of the user's exploration. The system presents this ordered list to the user as navigation guidance information. The entire process, starting from the logging of the exploratory search history, till the navigation guidance information generation, is based on the collaborative problem solving model. This model contains a collaborative memory based on a hierarchic structure, and implements the collaborative-problem-solving's result sharing mechanism. 2. Research Goal The purpose of this study is to propose a framework to support exploratory learning based a-priori knowledge embedded in a semantic feature map of the hyperspace and a user model of the learner's exploratory activity. To support the exploratory learning, our navigation system realizes the following sub-goals :