KG Learning

Knowledge graph (KG) learning focuses on effectively utilizing and expanding the vast information contained within KGs, aiming to improve knowledge representation, reasoning, and integration across different sources. Current research emphasizes enhancing KG embedding methods, such as graph neural networks and subgraph-based approaches, to better capture complex relationships and improve tasks like entity alignment and question answering. These advancements are crucial for improving various applications, including cross-lingual knowledge fusion and building more robust and intelligent question-answering systems. Addressing challenges like integrating expert knowledge and handling uncertainty within KG learning remains a key focus for achieving more human-like reasoning capabilities.