Intelligent Ontology CBR System for Fault Diagnosis and Repair

Abstract

Previous research in decision making has proposed the use of a case based reasoning approach to accumulate, organize, preserve, link and share diverse knowledge coming from past experiences. However, existing CBR systems lack semantic understanding, which is important for intelligent knowledge retrieval in decision support system. To develop an intelligent CBR system which can not only carry out data matching retrieval, but also perform semantic associated data access, and improve the traditional keyword-based search. .An effective case representation method as well as an appropriate case retrieval approach must be found. Ontology technology is an ideal selection for realizing our system because owing to the good semantic understanding offered by ontology. Thus, we adopt ontology approach as a means to acquire domain knowledge and construct a case-base and use ontological semantic retrieval as the case retrieval method. The resulting ontology based CBR tool is experimented in fault diagnosis and repairing domain, a semi-structured decision-making environment involving multiple attributes. The results showed the feasibility and the applicability of our approach, and the benefit of the ontology support.

Physical Internet Based Ontology for Supporting Traceability in Logistic IoT

“…The ontology development methodology is usually composed of several strategies on defining classes and class hierarchy, defining properties and naming considerations. Three types of ontologies are generally developed: Domain, task and application ontologies [34] [35]. Our approach to product traceability is supported by a domain ontology.…”