Abstract
Case-Based Reasoning (CBR) is a powerful tool for decision making as it approaches human natural thinking process, based on the reuse of past experiences in solving new problems. 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 when 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 viewed as a KI-CBR system based on domain ontology, built around jCOLIBRI and myCBR, two well-known frameworks to design KI-CBR systems. During the prototyping process, the use and functionality of the two focused frameworks are examined. A comparative study is performed with results presenting advantages provided by the use of ontologies with CBR systems and demonstrating that jCOLIBRI is well adapted to design KI-CBR system.