An Ontology-based Personalized Retrieval Model Using Case Base Reasoning

Abstract A novel ontology-Based Personalized Retrieval model using the Case Base Reasoning (CBR) tool is designed and presented in this paper. The proposed approach is aimed at achieving a scalable and user friendly data retrieval system with high retrieval performance where search results are ranked based on user preferences. The proposed retrieval framework integrates the advantages of two methods, a content-based method (ontology) to represent data and a case-based method (CBR) to personalize the search process and to provide users with alternative documents recommendations. To analyze the performance of the proposed approach, computer experiments are carried out using recall-precision curve and average precision (AP) metric. The performance of our approach is then compared to a framework that uses the classic vector space model. Results clearly indicate the strength of the proposed approach as well as its ability to accurately retrieve pertinent information. The proposed approach is particularly promising in applicable related to city logistics, especially in the field of itinerary research for urban freight transport.