Integrating Fragmented Standards Through Semantic Harmonization: A Large Language Model‐Driven Knowledge Graph Framework for Natural Resource Governance

ABSTRACT

Natural resource governance relies on standardized specifications for systematic management, yet current Natural Resource Standards and Specifications (NRSS) suffer from fragmentation, outdated content, and inadequate integration, hindering their synergistic application. This study proposes a semantic harmonization framework that integrates fragmented standards through a large language model‐driven knowledge graph. We develop a multi‐granular knowledge structure comprising four hierarchical layers: knowledge source layer, core concept layer, secondary concept layer, and foundation layer. The framework employs few‐shot prompting for document‐level knowledge extraction and Chain‐of‐Thought reasoning for clause‐level knowledge extraction. Experimental results demonstrate that our method significantly outperforms baseline models, with the Qwen3‐8B model achieving performance comparable to or exceeding the Qwen3‐32B model on key metrics. The proposed framework effectively consolidates fragmented standard resources, enhances semantic understanding, and provides precise knowledge services for natural resource governance tasks including surveys, monitoring, rights confirmation, asset accounting, and ecological protection. This work offers a technical pathway for the digital transformation and intelligent application of NRSS, supporting high‐quality development in resource management.