Abstract:
The main goal of this study is to design and develop self-learning educational legal expert system using Case-based Reasoning (CBR). This thesis resides on the system design, implementation, and evaluation of case based legal expert-system in the case of academic staffs in higher educational institutions. The objectives have been reviewed and priorities have been given to the process case acquisition and representation then attribute selection and appropriate mapping of user query to the case base which is the basis for recommendation. The application employed in this thesis is jCOLIBRI Framework developed by Juan A. Racio, for the implementation. The case base incorporates a total of 36 cases related to legal cases of Academic staffs in higher educational institutions. The application employed K-nearest neighbor (KNN) for retrieval of K cases. The performance of the application was tested using small sample of users and queries which achieves promising performance with evaluation average of 81% performance.
