Enhancing car damage repair cost prediction: Integrating ontology reasoning with regression models

The estimation of repair costs for car damage is a critical yet challenging task for insurance companies and repair shops. Accurate and the rapid predictions are essential for providing reliable cost estimates to customers. Traditional methods in this domain face multiple challenges, including manual processes and inaccuracies in repair cost estimation, as outlined in our article.

This paper introduces a novel approach that combines regression models with ontology reasoning to enhance the accuracy of car damage repair cost predictions. An Ontology for Car Damage (OCD)¹ ${}^{,}$² has been developed, which is meticulously structured and populated using Named Entity Recognition (NER) and Relation Extraction (RE) techniques. This ontology provides a comprehensive framework for organizing and understanding the complex domain of car damage, capturing essential semantic relationships and variables that significantly influence repair costs. By integrating OCD with seven regression models, such as Random Forest and Decision Tree, we have proposed a hybrid methodology that leverages both structured data and semantic understanding. Our approach not only accounts for typical variables such as the type and severity of damage, and labor costs but also identifies novel features through the use of SWRL (Semantic Web Rule Language) rules, enhancing the model’s predictive capabilities.

The performance of our models was evaluated using a substantial real-world dataset comprising over 300,000 records. This evaluation used metrics such as mean absolute error (MAE), root mean squared error (RMSE), and R-squared. The results indicate that our hybrid approach, which incorporates ontology reasoning, significantly outperforms traditional regression models.

The Random Forest model, especially when combined with the OCD ontology, showcased superior performance, exhibiting a minimal average deviation from the actual repair costs and achieving a low MAE.

This study’s findings demonstrate the potential of combining ontology reasoning with machine learning techniques for precise cost prediction in the automotive repair industry. Our methodology offers a robust tool for insurance companies and repair shops to generate more accurate, reliable, and automated cost estimates, ultimately benefiting both businesses and customers.