Semantic-Driven Data Augmentation for Improved Machine Learning Predictions (Extended Abstract)

Machine learning (ML) models frequently struggle in domains where labeled data is limited or sensitive. To address this challenge, we explore a semantic-driven data augmentation approach that incorporates external knowledge into tabular datasets. Our method leverages neuro-symbolic techniques to enrich training data with structured context derived from knowledge graphs (KGs), aiming to enhance the predictive capabilities of standard ML algorithms. We evaluate multiple approaches for integrating KG information into ML pipelines and examine their impact on model performance across binary classification tasks involving medical datasets such as heart disease and chronic kidney disease. The experimental setup includes four ML models and four distinct KG embedding algorithms, with performance evaluated using accuracy and F2 score. Results show that augmenting tabular features with semantic distance metrics from KG embeddings yields notable improvements. For instance, XGBoost achieves a significant F2 score increase from 75.19% to 90.85% in heart disease prediction.