KBQA Model

Knowledge Base Question Answering (KBQA) aims to automatically answer natural language questions using structured knowledge from knowledge bases. Current research focuses on developing more robust and efficient models, including end-to-end approaches that directly integrate knowledge base structure during query generation, few-shot learning methods for handling limited training data and unanswerable questions, and multi-agent systems leveraging large language models. These advancements are improving the accuracy and efficiency of KBQA systems, with significant implications for information retrieval, question answering systems, and other applications requiring knowledge-based reasoning.