An Ontology-Based Text Mining Method to Develop D-Matrix

In this point, we demonstrate an ontology based text mining method for naturally developing and updating a D-matrix by mining a huge number of repair verbatim (written in unstructured text) gathered during the analysis scenes. Fault dependency (D) -matrix is a systematic demonstrative model [8] which is used to catch the progressive system level deficiency symptomatic data comprising of dependencies between observable symptoms and failure modes connected with a framework. D- Matrix is a time consuming process. Developing a D-matrix from first standards and updating it utilizing the domain information is a work concentrated. Further, in-time increase of D-matrix through the disclosure of new symptoms and failure modes watched for the first run is a challenging task. In this methodology, we first develop the fault diagnosis ontology comprising of concepts and relationships regularly saw in the fault diagnosis domain. Next, we utilize the text mining algorithm that make utilization of this ontology to distinguish the fundamental artifacts, for example, parts, symptoms, failures modes, and their conditions from the unstructured repair verbatim text. The proposed technique is implements as a prototype tool and accepted by utilizing real - life information gathered from the automobiles space