Tuple Generating Dependency

Tuple-generating dependencies (TGDs) are rules used to infer new facts from existing data, particularly within knowledge bases and graph databases. Current research focuses on efficiently handling high-order dependencies—relationships involving multiple interconnected entities—using techniques like graph neural networks, chase procedures (algorithms for inferring new facts), and optimization methods for feature selection. This work aims to improve the accuracy and efficiency of knowledge representation and reasoning, impacting applications such as biomedical data analysis, natural language processing, and database management by enabling more accurate and interpretable models.