ForPKG-1.0: A Framework for Constructing Forestry Policy Knowledge Graph and Application Analysis

2024-11-17

arXiv.org

AI Summary

The forestry policy domain lacks systematic methodologies for knowledge graph construction. Method: This paper proposes the first fine-grained, scalable knowledge graph framework for intelligent governance in forestry policy. It (1) designs a novel domain-specific ontology enabling multi-level semantic modeling; (2) introduces an unsupervised policy information extraction method that significantly improves entity and relation extraction performance; and (3) establishes an end-to-end graph construction pipeline, integrating the policy knowledge graph into the Retrieval-Augmented Generation (RAG) paradigm for the first time to validate its effectiveness in enhancing large language model (LLM) reasoning. Contribution/Results: The resulting high-quality, open-source knowledge graph is publicly released on GitHub, supporting policy-focused question answering and LLM-based inference. This work establishes a new paradigm for structured policy knowledge organization and AI-driven governance in forestry.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Ontologies
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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 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. Our data is provided on Github https://github.com/luozhongze/ForPKG.

Problem

• Lack of research on forestry policy knowledge graph construction methods
• Need for fine-grained ontology and unsupervised extraction techniques
• Application value in large language model reasoning tasks

Innovation

• Fine-grained forestry policy domain ontology design
• Unsupervised policy information extraction method
• Complete forestry policy knowledge graph construction