An ontology based text mining system for knowledge discovery from the diagnosis data in the automotive domain

• An ontology based text mining system is proposed to handle millions of unstructured repair verbatim in automotive industry.
• Three novel components are developed in the proposed system – document annotation, semantic extractor, and frequently co-occurring term based clustering algorithm.
• The best practice repair actions can be discovered to fix the symptoms observed with a part failure to perform efficient fault diagnosis and root-cause investigation.
• The performance of the system has been validated on the real life data and the system is implemented in real life set up.

In automotive domain, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis (FD) process. Here, the aim of knowledge discovery using text mining (KDT) task is to discover the best-practice repair knowledge from millions of repair verbatim enabling accurate FD. However, the complexity of KDT problem is largely due to the fact that a significant amount of relevant knowledge is buried in noisy and unstructured verbatim. In this paper, we propose a novel ontology-based text mining system, which uses the diagnosis ontology for annotating key terms recorded in the repair verbatim. The annotated terms are extracted in different tuples, which are used to identify the field anomalies. The extracted tuples are further used by the frequently co-occurring clustering algorithm to cluster the repair verbatim data such that the best-practice repair actions used to fix commonly observed symptoms associated with the faulty parts can be discovered. The performance of our system has been validated by using the real world data and it has been successfully implemented in a web based distributed architecture in real life industry.