Amin, A. (2019). Building Intelligent Semantic Educational System (ISES) Based on Ontology and Semantic Web Mining.. International Journal of Intelligent Computing and Information Sciences, (), -. doi: 10.21608/ijicis.2019.5260.1002
Ahmed E Amin. "Building Intelligent Semantic Educational System (ISES) Based on Ontology and Semantic Web Mining.". International Journal of Intelligent Computing and Information Sciences, , , 2019, -. doi: 10.21608/ijicis.2019.5260.1002
Amin, A. (2019). 'Building Intelligent Semantic Educational System (ISES) Based on Ontology and Semantic Web Mining.', International Journal of Intelligent Computing and Information Sciences, (), pp. -. doi: 10.21608/ijicis.2019.5260.1002
Amin, A. Building Intelligent Semantic Educational System (ISES) Based on Ontology and Semantic Web Mining.. International Journal of Intelligent Computing and Information Sciences, 2019; (): -. doi: 10.21608/ijicis.2019.5260.1002
Building Intelligent Semantic Educational System (ISES) Based on Ontology and Semantic Web Mining.
Articles in Press, Accepted Manuscript, Available Online from 07 June 2019
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, when there are individual differences between instructors and learners in the previous backgrounds and levels of knowledge. In intelligent education systems, four types of ontology are identified that represent knowledge: students, content, education, and structure. There is a promising approach for satisfying the intelligent education systems requirements, which is one of the most prominent semantic Web ideas, based on commonly shared meaning (ontology) and metadata processed automatically. This approach can be supported using semantic querying and conceptual navigation for learning materials. One of the important results of this paper is the depth of most of the inferences derived from learning ontologies. Relations such as "has_Part" and "is_Part_Of" and their interplay can be easy dealing by the users. Therefore, this type of thinking can be presented through an automatic mechanism, and semantic information is necessary for reasoning about an automatic occurrence.
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