通过整合检索增强生成和知识图表来构建智能决策支持系统
Sili Wang1,2, Heng Yang3,4, Guangzu Bai3,4
1Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, 730000, Lanzhou, China. wangsl@llas.ac.cn.
Scientific reports
|October 10, 2025
概括
本研究引入了智能决策支持系统的新框架,结合了检索增强生成和知识图. 这种方法提高了复杂的跨领域查询的准确性,透明度和推理.
科学领域:
- 人工智能的人工智能
- 决策支持系统是什么?
- 知识表示 知识表示
背景情况:
- 当前的决策支持系统在上下文理解,透明度和多步骤,跨领域推理方面扎.
- 像Mistral 7B和LLaMA-2这样的生成模型在深度上下文理解和透明推理方面存在局限性.
- 现有的知识图方法可能缺乏生成模型的动态适应性.
研究的目的:
- 为智能决策支持系统 (IDSS) 提出一个新的框架.
- 克服当前IDSS在上下文理解,透明度和多领域推理方面的局限性.
- 提高决策准确性,推理透明度和上下文相关性.
主要方法:
- 开发了一个新的框架,将检索增强生成 (RAG) 模型与知识图 (KG) 集成在一起.
- 实现了灵活的知识编排层,以优化RAG和KG组件之间的信息交换.
- 评估了金融服务,医疗保健管理和供应链领域的框架.
主要成果:
- 与独立的RAG或KG方法相比,在决策准确性,推理透明度和上下文相关性方面取得了显著的改进.
- 在处理跨领域推理和模两可的查询方面展示了卓越的性能.
- 综合框架有效地解决了复杂的企业决策挑战.
结论:
- 拟议的框架提供了一种协同方法,利用生成人工智能和结构化知识的优势.
- 这项研究为下一代IDSS提供了路线图,能够提供可解释和上下文意识的建议.
- 该系统有效地将上下文信息与领域专业知识相结合,以提供强大的决策支持.
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