Advanced Analytics Center of Expertise

An ontology-based text mining and semantic 

similarity system for knowledge discovery in the 

automotive domain

Dnyanesh Rajpathak, Ph.D.

Staff Researcher

Advanced Analytics Center of Excellence, 

Chief Data Analytics Office

Email: dnyanesh.Rajpathak@gm.com
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Different stages of vehicle life cycle and data collection

Design Manufacturing Pre-launch Launch Warranty

DFMEA /

Requirements

Station to 

Station

End of line

Pre-launch 

Verbatim

Telemetry

Verbatim

Telemetry

Verbatim

Telemetry

Telemetry

“Ignition On/Ignition Off”

Diagnostic Trouble Codes (DTCs)

P0300 | B0001 | U1040 | C0550
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Key challenges associated with the text data

 Heterogenous data sources

– Technical, non-technical, and survey 

 Lean language

– ‘Engine Control Module’ vs ‘Powertrain Control Module’

 Surface representation consists of limited/no overlap

– ‘Vehicle Loss of Power’ vs ‘PCM P0300’

 High volume data (hundred of millions)

– Curse of dimensionality

 Different types of noises

– Misspelling, run-on-word, additional whitespaces, abbreviations, etc.
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Data cleaning pipeline is developed to pre-process the data

Prewarranty

Warranty

External

(e.g., National Highway 

Traffic Safety 

Administration)

TR * THE CONTACT STATE BRAK LINE FAILUREDUETOCORROSION. OBSERVED

P25A2, C05B0. AFTER 0.8HRS OF INSPECTION ALL BRAKE LINES ARE BADLY

RUSTED. REPLACED ALL LINES *TR

FUEL SENSOR DOES NOT WORK! AFTER FILLING GAS TANK COMPLETELY, FUEL

GAUGE STILL READS EMPTY. GAUGE CAN SOMETIMES READ UP TO HALF, THEN

DROPS TO A QUARTER, THEN JUST DROPS TO EMPTY WHENEVER IT PLEASES.

ALSO, SOMETIMES THE SPEEDOMETER ACTS VERY ?TWITCHY?. I HAVE READ

MULTIPLE COMPLAINTS ONLINE ABOUT THESE SAME EXACT ISSUES AND FEEL

THAT A FULL RECALL IS ABSOLUTELY NECESSARY. *TR

Cleaned Unstructured 

Data
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Business problems

 Knowledge formalism

– Automatic ontology learning from warranty 

data

 Validation & Verification 

– Automatic linking of requirements using 

semantic similarity

 Diagnosis & Prognosis

– Automatic development of fault dependency 

matrix

– Text mining for knowledge discovery from 

the warranty data

 Continuous Improvement

– Augmentation of DFMEAs considering new 

symptoms and failure modes discovered 

from the warranty data
Advanced Analytics Center of Expertise 6

An ontology-based text mining system for knowledge 

discovery

Warranty

Data

Service/Quality

“CS Vehicle with loss of power. Detected leaks in cooler line, it was loose, 

repaired”

Frequent 

Failures  Best 

Practice Repairs

Legacy 

Issues

Dealer 

Training Need

SCAN 

TOOLS
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An ontology-based text mining system for knowledge 

discovery

Bi-directional LSTM

Bi-directional LSTM

Transfer Learning 
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Results

Precision: Frequent Failure  Symptoms  Best Practice Repairs Recall: Frequent Failure  Symptoms  Best Practice Repairs

F1: Frequent Failure  Symptoms  Best Practice Repairs
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Drill down analysis for knowledge discovery

 Suppliers

 Manufacturing plants

 Platform level analysis
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Automatic development of fault dependency matrix
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Results

PPDD =

∑ii=11

mm DDii

mm

The fault detection (PD) is defined as the percent 

of faults detected by the symptoms by observing 

the failure modes associated with a system.

Comparison of fault detection between historical Data-driven Dmatrix vs. Text-driven D-matrix

FFII =

∑ii=11

mm IIIIIIii

mm

The fault isolation (FI) is the probability that the 

symptoms uniquely isolate the faults of a system 

for the failure modes associated with a system.

Comparison of % fault isolation between historical Data-driven Dmatrix vs. Text-driven D-matrix
Advanced Analytics Center of Expertise 12

Publications

Rajpathak, D. G. (2013). An Ontology-Based Text Mining System for Knowledge 

Discovery from the diagnosis data in the automotive domain. Computers in 

Industry, vol. 64 (5), pp. 565-580

Rajpathak, D., Chougule, R., and Bandyopadhyay, P. (2012). A domain specific 

decision support system for knowledge discovery using association and text 

mining. Knowledge and Information Systems, vol. 31, pp. 405-432
Advanced Analytics Center of Expertise 13

Publications

Rajpathak, D. G. and Singh, S. (2014). An Ontology-Based Text Mining 

Method to Develop D-Matrix From Unstructured Text. IEEE Transactions 

on Systems, Man, and Cybernetics, vol. 44 (7), pp. 966-977

Rajpathak, D., Xu, Y., and Gibbs, I. (2020). An integrated framework for 

automatic ontology learning for unstructured repair text data for 

effective fault detection and isolation in automotive domain. Computers 

in Industry, vol. 123, pp. 103338
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Thank you