Semantic Knowledge

Semantic knowledge research focuses on how machines can understand and utilize the meaning behind words, concepts, and relationships, aiming to bridge the gap between human understanding and computational representation. Current research heavily utilizes large language models (LLMs) and graph neural networks (GNNs) to encode and process semantic information from text and knowledge graphs, often integrating this knowledge into tasks like knowledge tracing, recommendation systems, and continual learning. This work is significant because improved semantic understanding is crucial for advancing artificial intelligence across numerous domains, leading to more robust and interpretable systems with enhanced capabilities in natural language processing, computer vision, and knowledge representation.