Intelligent question answering for water conservancy project inspection driven by knowledge graph and large language model collaboration

Engineering inspection is of great significance to ensure the safe operation of the project. However, the unclear query statements of the detectors pose a challenge to the intelligent question answering task. Existing knowledge graph-based question-answering systems face issues of vocabulary limitations and reliance on fixed templates. Solely relying on Large Language Models (LLMs) for questioning introduces noise and randomness due to their extensive knowledge base. Therefore, this paper proposes a novel approach that synergistically employs both knowledge graphs and LLMs for intelligent question-answering in hydroengineering inspection. The method divides the overall task into five units, progressively clarifying query statements for accurate answers. Leveraging LLM’s vast prior knowledge, robust semantic understanding, and contextual learning mitigates issues related to vocabulary limitations and template dependence. Simultaneously, the knowledge contained in the graph is integrated into an optimal clarification path and transferred to LLM to address noise and randomness, thereby enhancing the efficiency of the clarification process. Benchmark experiments demonstrate that the proposed method achieves Mean Reciprocal Rank (MRR), Mean Average Precision (MAP), Precision, and Recall metrics all above 0.73. The results affirm the method’s effectiveness in improving the accuracy of intelligent question-answering in hydroengineering inspection, with potential implications for similar applications in other domains of hydraulic engineering.