Semantic Graph

Semantic graphs represent information as nodes and edges, aiming to capture the meaning and relationships within data, improving various tasks from natural language processing to computer vision. Current research focuses on developing efficient algorithms for generating and processing these graphs, often employing graph neural networks (GNNs) and transformer architectures, and integrating them with other models to enhance performance in tasks like link prediction, scene graph generation, and question answering. The ability to effectively represent and reason with semantic graphs has significant implications for improving the accuracy, interpretability, and efficiency of AI systems across numerous domains.