Graphical Analysis on Text Mining Unstructured Data Using D-Matrix

Fault dependency (D-matrix) is used as a diagnostic model that identifies the fault system data and its causal relationship at the hierarchical system-level. It consists of dependencies and relationship between identified failure modes and symptoms related to a system. Constructing such D-matrix fault detection model is time overwhelming task .A system is proposed that describes associate ontology based text mining on unstructured data using D-matrix for automatically constructing D-matrix by mining many repair verbatim text data (typically written in unstructured text) collected throughout the identification process. And also graphical model generation for each generated D-matrix. Initially we construct fault diagnosis ontology and then text mining techniques are applied to spot dependencies among failure modes and identified symptom. D-matrix is represented in graph so analysis gets easier and faulty parts becomes simply detectable. The proposed methodology are implemented as a prototype tool and validated by using real-life information collected from the automobile domain

Development of Graph from D-matrix based on Ontological Text Mining Method

Fault dependency (D-matrix) is a diagnostic model that catches the fault system data and its causal relationship at the hierarchical system-level. It consists of dependencies and relationship between observable failure modes and symptoms associated with a system. Constructing such D-matrix fault detection model is time consuming task. In this paper, a system is proposed that describes an ontology based text mining method for automatically constructing D-matrix by mining hundreds of repair verbatim text data (typically written in unstructured text) collected during the diagnosis episodes. First we construct fault diagnosis ontology and then text mining techniques are applied to identify dependencies among failure modes and observable symptom. D-matrix is represented in graph so that analysis gets easier and faulty parts becomes easily detectable. The proposed method will be implemented as a prototype tool and validated by using real-life data collected from the automobile domain. DOI: 10.17762/ijritcc2321-8169.15055