Semantic-Based Data Augmentation for Machine Learning Prediction Enhancement

Machine learning (ML) methods have demonstrated strong predictive capabilities when trained on large datasets. However, in domains where data is scarce or sensitive, ML models often exhibit sub-optimal performance. Our hypothesis is that semantically enriching the available training dataset can enhance the predictive power of ML models, particularly in data-scarce scenarios. To investigate this hypothesis, we propose novel neuro-symbolic approaches that augment tabular data with knowledge graph (KG) information, providing additional context and structure to improve model performance. Concretely, we introduce and examine several integration techniques of KG information through embeddings and explore how different KG embedding algorithms affect model performance, with a specific focus on accuracy and F2 scores. Our evaluation involves four distinct ML algorithms and four KG embedding techniques. We apply our approach to binary classification tasks on tabular data, including heart disease and chronic kidney disease. Our experimental results show improvements in performance particularly when tabular data is augmented with distance features computed in the embedding space. Notably, we achieve gains in F2 scores, such as an increase in XGBoost performance from 75.19% to 90.85% for heart disease prediction. These findings demonstrate the potential of KG-based augmentation to enhance ML performance.