Concept Graph

Concept graphs represent data as interconnected nodes (concepts) and edges (relationships), offering a powerful framework for knowledge representation and reasoning across diverse domains. Current research focuses on leveraging concept graphs for tasks like bias detection and mitigation in machine learning datasets, enhancing vision-language models, and improving question answering systems, often employing graph neural networks and large language models. This approach facilitates improved model interpretability, enables more nuanced analysis of complex data, and holds significant promise for applications ranging from medical image analysis to personalized user experience design.