Enhancing Automotive Safety through Context-Aware Ontology Classification

Abstract

Dynamic contextual feature extraction has emerged as a critical approach for classifying unstructured automotive safety data into domain-specific ontologies. This article presents a novel framework that leverages part-of-speech tagging, positional probabilities, and optimized feature vectors to process diverse safety datasets effectively. This methodology introduces adaptive context windows and domain-aware feature extraction techniques, demonstrating marked improvements in classification accuracy compared to traditional approaches. This article shows a substantial enhancement in pattern recognition and fault classification capabilities through extensive evaluation using real-world automotive fault logs, crash reports, and service feedback data. The system particularly excelled in identifying subtle correlations between sensor anomalies and environmental conditions, while maintaining robust performance across varying data contexts. This article advances the field of automotive safety data processing by providing a scalable, context-aware solution that addresses the growing complexity of modern vehicle systems. This article suggests promising applications in real-time safety monitoring and predictive maintenance systems.