Large Language Model-Driven Knowledge Graph Construction and Restoration Measure Decision Support for Sustainable Mine Ecological Restoration

Mine ecological restoration involves multiple tasks, including ecological problem identification, restoration measure selection, and restoration effect evaluation. Relevant knowledge is widely scattered across academic literature and policy and regulatory documents, making it difficult to directly support structured queries, knowledge association, and measure screening. To address the insufficient organization of knowledge in mine ecological restoration and the unstable quality of triples extracted by large language models, this study proposes a large language model-driven workflow for knowledge graph construction and restoration measure decision support in mine ecological restoration. First, a weak ontology schema containing eight entity types and eight relation types was constructed, and a manually annotated dataset was developed based on 30 papers on mine ecological restoration. Second, multiple large language models were compared on the triple extraction task, and Kimi, which achieved the best overall performance, was selected for large-scale knowledge extraction. On this basis, a weak-ontology-guided triple quality control method, named WOTQC, was proposed to check the structural consistency of candidate triples in terms of entity type, relation type, relation direction, and entity orientation. DeepSeek was then introduced to perform verification based on original textual evidence. Subsequently, entity normalization and relation aggregation were conducted on the verified triples to construct a knowledge graph for mine ecological restoration. Path mining, restoration measure decision support, and external case validation were further performed around the “ecological problem–restoration measure–restoration effect” path. The experimental results show that Kimi-based initial extraction achieved an F1 score of 0.7286. After WOTQC processing, the F1 score increased to 0.7561, and after DeepSeek-based evidence verification, it further improved to 0.7862. The final knowledge graph contains 18,783 entity nodes and 23,149 relations, and identifies 3048 “ecological problem–restoration measure–restoration effect” paths. The external case validation shows that the integrated ranking method achieved 1.00 under both strict Hit@3 and strict Hit@5, with a path explanation coverage@5 of 0.7500. The ranking results of the knowledge graph showed good correspondence with the main engineering measures in an actual mine ecological restoration design scheme. The results indicate that weak ontology constraints and verification based on original textual evidence can improve the reliability of triple extraction, while entity normalization and relation aggregation can enhance the structural consistency of the knowledge graph. The constructed knowledge graph can provide traceable and interpretable knowledge support for knowledge organization, candidate restoration measure screening, and restoration scheme formulation in mine ecological restoration, thereby supporting more informed and evidence-based restoration decision-making and contributing to the long-term ecological recovery and sustainable management of degraded mining areas.