Comparative analysis of case retrieval implementation for knowledge intensive CBR application

The Case-Based Reasoning (CBR) is a problem solving approach based on reuse by analogy from past experiences. A CBR system is a combination of processes and knowledge called “knowledge containers”, Its reasoning power can be improved through the use of domain knowledge. CBR systems combining case specific knowledge with general domain knowledge models are called Knowledge Intensive CBR (KI-CBR). Although CBR claims to reduce the effort required for developing knowledge-based systems substantially compared with more traditional Artificial Intelligence approaches, the implementation of a CBR application from scratch is still a time consuming task. The present work aims to develop a CBR application for fault diagnosis of steam turbines that integrates a domain knowledge modeling in an ontological form and focuses on the similarity-based retrieval step. This system is view as a KI-CBR system based on domain ontology, built around jCOLIBRI and myCBR a well-known frameworks to design KI-CBR systems. During prototyping, we examine the use and functionality of the focused frameworks. A comparative study is performed with results of presenting a successful retrieval task and demonstrating that jCOLIBRI and myCBR are well adapted to design KI-CBR system.