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AN ONTOLOGY TEXT MINING TO CONVERSION OF UNSTRUCTURED TO STRUCTURE TEXT IN D-MATRIX

In the general context of knowledge discovery, that technique called textual content mining technique, are important to extract facts from unstructured textual information. The extracted records can then further used to classify the content of massive textual bases. This paper discusses Fault dependency (D)-matrix is a systematic diagnostic version that desires to capture the hierarchical device-degree fault diagnostic information. It including dependencies between observable symptoms and failure modes related to a system. Every time person type any query for looking any report or information, most possibly all of the files or records looking to search query with title of to be had information and building a D-matrix from first concepts and updating it the usage of the domain know-how is a hard work intensive and time consuming project. Similarly, in-time augmentation of D-matrix through the revelation of new signs and symptoms and failure modes found for the first time is a hard challenge. Proposed machine describes an ontology primarily based text mining approach for automatically constructing and updating a D-matrix with the aid of mining loads of thousands of restore verbatim(generally written in unstructured text). In proposed technique, firstly construct the fault analysis ontology such as ideas and relationships typically observed within the fault prognosis area. Next, hire the textual content mining algorithms that employ ontology concept to become aware of the wanted artifacts, inclusive of additives, signs, failure modes, and their dependencies from the unstructured restore verbatim textual content.