| Summary: | 碩士 === 國立中正大學 === 資訊工程研究所 === 92 === With the evolution of learning technology, the making of digital teaching materials becomes more and more easily. As a large number of course materials were produced, how to manage them in a well-arranged style turns into a oncoming problem. In this thesis, we propose to design a course resource management system based on Semantic Web technology that can classify course materials, extract concept ontology from them and combine course ontology with user preference to improve searching precision. This system consist of three parts: material processing subsystem, personalization subsystem and ontology inference subsystem. Material processing subsystem provides interfaces for users to upload, retrieve and search course material according to user preference ontology which get from ontology inference subsystem. Personalization subsystem uses feature selection machine learning algorithm to analyze user access log and generates user preference data. Ontology inference subsystem converts user preference data to user preference ontology by the ontology inference rule defined by us and then makes search functionality with personalized characteristic. By combination of the three subsystems, we not only can construct a knowledge-based course resource management system but also can build a environment with personalized feature.
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