An Ontology Based Method for Data Validation

The rapid advancement of artificial intelligence (AI) technologies has highlighted a critical imbalance in the acquisition of high-quality training data, which remains a significant bottleneck. Despite breakthroughs in model architectures and algorithms, outdated data engineering practices and the monopolization of premium datasets by major tech companies hinder progress. To address the urgent need for automated, demand-driven data assessment systems that facilitate customizable labeling and semantic-aware filtering of data, we propose an end-to-end data processing method that combines model recognition and knowledge inference, enabling adaptive rules for filtering irrelevant data based on varying requirements. By leveraging techniques such as Faster R-CNN for object detection and integrating ontology reasoning through OWL and SWRL, our method aims to maximize data utility while ensuring accuracy in recognition tasks. Experimental results demonstrate the effectiveness of our approach in enhancing data curation processes, ultimately contributing to more reliable AI systems capable of navigating complex real-world scenarios.