KG Question Answering

Knowledge graph question answering (KG-QA) aims to enable computers to answer complex questions by leveraging structured knowledge stored in knowledge graphs. Current research focuses on developing more efficient and accurate end-to-end models, often employing techniques like reinforcement learning to improve multi-hop reasoning and integrating large language models for enhanced logical inference and SPARQL query generation. These advancements are crucial for improving access to and understanding of complex information, with applications ranging from semantic search and personalized assistants to fact-checking and advanced reasoning systems.