Large Scale Knowledge Graph

Large-scale knowledge graphs (KGs) aim to represent vast amounts of structured information, enabling complex reasoning and question answering. Current research focuses on improving the efficiency and accuracy of KG reasoning, particularly through advancements in graph neural networks, embedding methods, and hybrid approaches combining language models with graph-based techniques to address challenges like subgraph retrieval, noisy data, and missing modalities. These improvements are crucial for various applications, including biomedical research, scientific discovery, and enhancing the factual accuracy of large language models.