A policy knowledge graph can provide decision support for tasks such as project compliance, policy analysis, and intelligent question answering, and can also serve as an external knowledge base to assist the reasoning process of related large language models. Although there have been many related works on knowledge graphs, there is currently a lack of research on the construction methods of policy knowledge graphs. This paper, focusing on the forestry field, designs a complete policy knowledge graph construction framework, including: firstly, proposing a fine-grained forestry policy domain ontology; then, proposing an unsupervised policy information extraction method, and finally, constructing a complete forestry policy knowledge graph. The experimental results show that the proposed ontology has good expressiveness and extensibility, and the policy information extraction method proposed in this paper achieves better results than other unsupervised methods. Furthermore, by analyzing the application of the knowledge graph in the retrieval-augmented-generation task of the large language models, the practical application value of the knowledge graph in the era of large language models is confirmed. The knowledge graph resource will be released on an open-source platform and can serve as the basic knowledge base for forestry policy-related intelligent systems. It can also be used for academic research. In addition, this study can provide reference and guidance for the construction of policy knowledge graphs in other fields.

翻译：政策知识图谱能够为项目合规、政策分析、智能问答等任务提供决策支持，也可作为外部知识库辅助相关大语言模型的推理过程。尽管已有许多关于知识图谱的相关工作，但目前仍缺乏针对政策知识图谱构建方法的研究。本文聚焦林业领域，设计了一套完整的政策知识图谱构建框架，包括：首先，提出了一种细粒度的林业政策领域本体；其次，提出了一种无监督的政策信息抽取方法；最后，构建了一个完整的林业政策知识图谱。实验结果表明，所提出的本体具有良好的表达能力和可扩展性，且本文提出的政策信息抽取方法相较于其他无监督方法取得了更好的效果。此外，通过分析该知识图谱在大语言模型的检索增强生成任务中的应用，证实了知识图谱在大语言模型时代的实际应用价值。该知识图谱资源将在开源平台发布，可作为林业政策相关智能系统的基础知识库，也可用于学术研究。此外，本研究可为其他领域的政策知识图谱构建提供参考与指导。