自然KG:通过Text2Cypher应用程序为自然金融提供了一个本体学和知识图
Neetu Kushwaha1,2, Alok Singh1,2,3, Hassan Aftab Sheikh1,2
1Smith School of Enterprise and the Environment, University of Oxford, Oxford, United Kingdom.
Frontiers in artificial intelligence
|December 22, 2025
概括
自然金融面临挑战,原因是缺乏将自然资本与金融资产映射出来的工具. 自然KG,一个新的知识图和微调的人工智能模型显示出对评估环境风险和依赖性的承诺.
科学领域:
- 环境金融 环境金融
- 知识表示 知识表示
- 人工智能的人工智能
背景情况:
- 自然金融需要强大的分析框架来评估风险,影响和依赖性.
- 当前的金融系统缺乏结构化的工具来将自然资本与金融资产联系起来.
- 自然金融的系统性弹性受到数据整合挑战的阻碍.
研究的目的:
- 介绍NatureKG,这是自然金融的第一个本体学和知识图.
- 开发人工智能模型,将自然语言查询转化为金融机构的可操作见解.
- 允许对环境风险,影响和金融方面的依赖进行系统评估.
主要方法:
- 设计了一个基于ENCORE,SBTN和文献的域本体,定义关键实体.
- 在NatureKG中实例化了本体学,这是一个Neo4j知识图,有320个节点和540个关系.
- 使用 Text2Cypher 数据集进行自然语言查询,微调了三种大型语言模型 (Phi-3,LLaMA-3.1-8B,Mistral-7B).
主要成果:
- 菲-3表现出卓越的性能,执行精度最高 (0.21),宏观F1得分最高 (0.56).
- 精心调整的较小模型显示在低资源,特定领域的自然金融环境中有效的概括.
- 验证了在自然金融应用中使用LLM辅助查询的可行性.
结论:
- 自然KG提供了一个基本的框架来表示和分析金融环境因素.
- 该研究为将特定领域的实体学与人工智能集成为可持续金融奠定了基础.
- 未来的研究应该专注于扩大数据集范围和完善AI模型推理能力.
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