Semantic Hierarchy

Semantic hierarchy research focuses on representing and utilizing the hierarchical relationships between concepts, improving the performance and explainability of various AI systems. Current efforts concentrate on integrating hierarchical semantic information into models for tasks like question answering, object detection, and knowledge graph completion, often employing techniques like hierarchical clustering, contrastive learning, and novel embedding methods in hyperbolic space. This work is significant because effectively leveraging semantic hierarchies enhances the accuracy, robustness, and interpretability of AI models across diverse applications, leading to more reliable and understandable systems.