KG-LLM Synergy for Intelligent Soil and Water Conservation Standard Governance

Existing soil and water conservation standards suffer from fragmentation, inconsistent cross-referencing, and limited machine interpretability, hindering efficient regulatory compliance and decision making. To address these challenges, we developed SwacGPT, an intelligent system that integrates domain-specific knowledge graph construction with large language models for enhanced standard interpretation and reasoning. Specifically, we constructed a domain-specific knowledge graph (SwacKG) using a hybrid approach that combines rule-based templates with a pre-trained BERT-based model. This graph systematically organizes conservation standards via multi-dimensional semantic relationships, with 87.8% entity extraction precision and 84.9% relation extraction precision, enabling precise data association across heterogeneous regulatory sources. SwacGPT leverages both the graph-structured knowledge from the SwacKG and original textual content to provide intelligent reasoning capabilities. For rigorous validation, a comprehensive evaluation dataset comprising both objective and subjective questions was designed. Experimental results show that SwacGPT achieves scoring rates of 78.67% on single-choice questions, 81.65% on multiple-choice questions, and 80.5% on subjective short-answer questions, ranking the best among the other five evaluated models. This demonstrates that the synergistic integration of domain-specific KGs with tailored LLMs creates an effective solution for intelligent environmental governance, providing critical decision support for land space optimization and cross-jurisdictional coordination in sustainable land management.