Knowledge graph-augmented large language model question answering based on intent recognition： A case study of flood defense and rescue

When enhancing the application of large language model （LLM） in flood defense and rescue with water conservancy knowledge graphs， intent recognition of user queries faces challenges such as limited corpora， an abundance of specialized terminology， and difficulties in semantic understanding. Existing methods perform poorly in low-resource intent recognition scenarios. This study proposes a multi-model ensemble method based on a voting strategy to accurately identify question intent and extract knowledge from knowledge graphs under low-resource conditions， leading to the development of a question-answering system for flood defense and rescue. Firstly， based on domain entity recognition and text semantic representation， three individual intent recognition models were constructed using rule-based methods， machine learning， and LLMs. Secondly， the Grey Wolf Optimization algorithm was used to assign weights to the individual models based on their performance， and a voting strategy was used to construct an intent recognition ensemble model. Finally， the ensemble model was subsequently employed to query the flood defense and rescue knowledge graph， and in combination with an LLM， a question-answering system was developed to facilitate efficient interaction between natural language queries and the knowledge graph. Experimental results show that the ensemble model achieves an average F1 score of 0.912 in five-fold cross-validation in low-resource intent recognition scenarios， markedly outperforming deep learning models such as BERT. The developed system enables accurate and efficient retrieval and reuse of domain knowledge， providing a new pathway for the transformation and utilization of water conservancy knowledge and the advancement of smart water management.