Large Knowledge Graph

Large knowledge graphs (KGs) are massive, interconnected datasets representing factual information, aiming to capture and reason with world knowledge. Current research focuses on improving KG completeness through rule learning guided by language models, developing efficient query languages for handling complex and conflicting information within RDF-star structures, and optimizing graph neural network (GNN) architectures for scalable training and inference on these large datasets. This work is crucial for advancing various applications, including semantic search, data analytics, and machine learning tasks like link prediction and node classification, while also addressing challenges related to factuality, security, and efficient knowledge representation.