relationaware
Relationaware (relationaware) is a term used in machine learning to describe models and representations that explicitly account for relationships among entities within data. It emphasizes relational structure, aiming to capture how items influence one another rather than treating elements in isolation.
Techniques associated with relationaware include graph neural networks, relational networks, and attention schemes designed to model
Applications span natural language processing tasks such as relation extraction and question answering, computer vision tasks
Relationaware work is part of broader research into relational reasoning and structured representations. It complements attribute-focused
Related topics include graph neural networks, relational reasoning benchmarks, scene graph generation, and knowledge graph embedding.