关键词: 大语言模型, 提示学习, 知识图谱, 知识抽取, 数字孪生水利建设

Abstract: Constructing digital twin water conservancy construction knowledge graph to excavate the potential relationship between water conservancy construction objects can help the relevant personnel to optimise the water construction design scheme and decision-making. Aiming at the characteristics of digital twin water conservancy construction with strong discipline intersections and complex knowledge structure, as well as the lack of learning of water conservancy domain knowledge in the general knowledge extraction model and insufficient knowledge extraction accuracy, a digital twin water conservancy domain knowledge extraction method based on a large language model is proposed in order to improve the accuracy of knowledge extraction. The method deploys a local large language model and integrates digital twin water conservancy domain knowledge through LangChain, fine-tunes the big language model based on learning, the large language model extracts knowledge using semantic understanding and generative capabilities, and optimises the entity extraction results by designing heterogeneous entity alignment strategies. Comparison experiments and ablation experiments are conducted on a water domain corpus to verify the effectiveness of the proposed method. The results of the comparison experiments show that compared to the deep learning-based BiLSTM-CRF named entity recognition model and the UIE model, the proposed method has better accuracy, with F1 scores of 88.63% and 84.46% for entity extraction and relationship extraction, respectively, and the accuracy rate of entity extraction reaches 90.11%. The results of the ablation experiments show that the proposed method improves the F1 scores of entity extraction and relation extraction by 5.5 percentage points and 3.2 percentage points, respectively, compared with the LLM baseline model. Therefore, the method achieves the construction of digital twin water conservancy construction knowledge graph on the basis of guaranteeing the quality of knowledge graph construction.

Key words: large language model, prompt learning, knowledge graph, knowledge extraction, digital twin water conservancy construction