Semantic Intelligence: Graph RAG-Driven Agents for Time Series Analytics

Alexander Graß

doi:10.1007/978-3-032-10486-1_21

2026-01-01

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DOI：10.1007/978-3-032-10486-1_21

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摘要

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本工作介绍了一种结合大型语言模型（LLMs）与知识图谱的新型框架，旨在提升时间序列分析中的自动化、可解释性和上下文推理能力。该框架允许用户通过直观的自然语言查询与时间序列数据集交互，从而从原始数据和语义上下文中提取可操作的见解。通过利用图检索增强生成（Graph RAG），该框架整合了结构化领域知识，提高了推理准确性，并促进了上下文感知的响应。此外，利用基于专用本体构建的知识图谱作为记忆机制，能够实现见解的增量积累，并支持对时间特征的迭代动态探索。该方法简化了不同专业水平用户对高级分析工具的访问，使他们能够更全面地理解时间序列数据。本文概述了该框架的架构，通过使用多种LLMs的评估测试了其性能，并讨论了未来潜在的改进方向。

Keyword:

Data Science

Large Language Models

Knowledge Graphs

Retrieval Augmented Generation

Data Analysis Agents

期刊

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING-IDEAL 2025, PT I

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论文数:

被引数:

机构

fraunhofer gesellschaft

学者数:

1.6W

论文数: 1.2W

被引数: 24

引用论文

Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark 评估大型语言模型在时间序列特征理解上的表现：一种全面的分类法和基准测试

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