Relational Context

Relational context, encompassing the relationships between entities within a dataset, is a crucial area of research aiming to improve the accuracy and efficiency of various machine learning tasks. Current efforts focus on incorporating relational information into models using techniques like transformer architectures, message-passing neural networks, and attention mechanisms, often enhancing existing methods such as relational joins or graph neural networks. This research is significant because effectively leveraging relational context leads to improved performance in diverse applications, including knowledge graph completion, scene graph generation, protein-protein interaction prediction, and human-object interaction detection, ultimately advancing our ability to extract meaningful insights from complex data.

Papers