This paper focuses on the design and implementation of a domain ontology-based navigation learning system, which can guide the user to learn more efficiently. Aiming at improving users' conceptual understanding of course material, a navigation structure was built among concepts, and each concept is presumed to have at least one concept instance which is provided by a Web answer system based on natural language which was integrated in this system. Then users would browse through the linked resources under those particular concepts as well as related concepts and get a better understanding of the material in the course. The kernel of such function was a domain knowledge base which was composed of concepts and relationship between them. Ontology was introduced as a mechanism to guide the establishment of this base. At last, a method of extracting concepts and relationship between them by using machine learning algorithms were presented.

The research work on question answering (QA) system is a very important field in e-learning. The factoid QA system has developed rapidly in recent years and it is basically a solved problem. However, how to answer questions beyond factoid is still a challengeable task. In this paper, we propose an approach for knowledge-oriented answer extraction beyond factoid. Furthermore, we focus on the answer classification step in the task of answer extraction. Experimental results show that our method is viable.