Study on an architecture of ontology-based task modeling and deduction for automotive troubleshooting service

The rapid development of today's smart automobile with increasing functionality and complexity, which results in rising requirement and cost for its troubleshooting service in the after-sales stage. Hence, to keep the firm's competitive strength, effective design knowledge utilization of automotive created from the task model can promote the feasibility making of a troubleshooting procedure by offering available relationships and semantic scheme of task. The proposed architecture primarily consists of base, field and application three tiers. A formalized representation of ontology, OWL, is used to organize the base filed. The filed tier includes extensional notions and relations for integration of troubleshooting and a criterion repository for verifying solution feasibility, which is depicted in SWRL. In the application tier, a deducing module is generated on the basis of ontology and criterion deduction. To enhance this semantics, in this research, a task modeling and deduction mechanism with feature-intensive ontology are proposed to clearly represent correlative notions for automotive troubleshooting planning (ATP). A criterion-based deducing module on the basis of OWL-DL and SWRL is also applied to specify implied relations through merging deducing modules (DMs) to deal with complex feature data. Eventually, this proposed architecture is examined and verified with an instance relevant to automotive troubleshooting procedure.