Knowledge Base Question Answering

Knowledge Base Question Answering (KBQA) aims to enable computers to answer natural language questions using structured knowledge from knowledge bases (KBs). Current research heavily focuses on improving the accuracy and efficiency of KBQA systems, particularly by leveraging large language models (LLMs) within various architectures, including semantic parsing, retrieve-then-reason, and generate-then-retrieve frameworks. These advancements address challenges like handling complex questions, diverse KB schemas, and unanswerable questions, ultimately aiming for more robust and generalizable systems. The impact of improved KBQA extends to enhanced information access for users and facilitates progress in areas like question answering, natural language understanding, and semantic parsing.