ForPKG: A Framework for Constructing Forestry Policy Knowledge Graph and Application Analysis
Jingyun Sun, Zhongze Luo

TL;DR
This paper presents a comprehensive framework for constructing a forestry policy knowledge graph, including ontology design and unsupervised information extraction, demonstrating its effectiveness and application in large language model tasks.
Contribution
It introduces a novel forestry policy domain ontology and an unsupervised method for policy information extraction, filling gaps in policy knowledge graph construction research.
Findings
Ontology has high expressiveness and extensibility.
Proposed extraction method outperforms other unsupervised approaches.
Knowledge graph enhances large language model applications.
Abstract
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…
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Taxonomy
TopicsSemantic Web and Ontologies · Advanced Graph Neural Networks · Graph Theory and Algorithms
MethodsBalanced Selection · Ontology
