Structured Knowledge

Structured knowledge research focuses on effectively integrating structured data, such as knowledge graphs and databases, with large language models (LLMs) to enhance their reasoning and knowledge representation capabilities. Current research emphasizes developing novel architectures and algorithms, including prompt engineering techniques, knowledge graph integration methods, and diffusion models, to improve information extraction, question answering, and knowledge base completion tasks. This work is significant because it addresses limitations of LLMs in handling structured information and promises to improve the accuracy, reliability, and explainability of AI systems across diverse applications, from medical diagnosis to scientific discovery.