This research presents a method to build an intelligent semantic education system based on two novel techniques namely ontology and semantic web mining. Ontologies are a powerful mechanism to solve a retrieval problem that is exacerbated rapidly within the modern educational systems, where that instructors and learners have very different backgrounds and levels of knowledge. In intelligent education systems, it makes sense to distinguish between four types of knowledge, and thus of ontologies: students, content, pedagogy, and structure. There is a promising approach for satisfying the intelligent education systems requirements, which is one of the main ideas of the Semantic Web, based on commonly shared meaning (ontology) and machine-processable metadata. It can support both semantic querying and the conceptual navigation of learning materials.
In this paper has reached an important result that most of the inferences drawn from learning ontologies can be expected to be very deep. Relations such as "has_Part" and "is_Part_Of" and their interplay can be easy dealing by the users. So that, this kind of reasoning can be exhibited by an automated mechanism, and the semantic information is necessary for reasoning to occur in an automated fashion.