KG Reasoning

Knowledge graph (KG) reasoning focuses on inferring new knowledge and answering complex queries from existing KG data, addressing the inherent incompleteness of these graphs. Current research emphasizes improving the efficiency and accuracy of reasoning, particularly for inductive tasks involving new entities and complex logical queries, utilizing models like graph neural networks (GNNs) and large language models (LLMs) often in conjunction. These advancements are significant for various AI applications, including question answering, knowledge completion, and decision-making systems, by enabling more robust and nuanced reasoning capabilities.