用大模型与知识图谱协作，实时分析复杂数据关系。
Multi-dimensional Data Analysis and Applications Basing on LLM Agents and Knowledge Graph Interactions
- 大模型代理自动提取非结构化数据，动态构建知识图谱。
- 支持用户交互式探索节点关系，提升分析深度。
- 适合产品生态、关系挖掘等需要实时洞察的场景。
在大数据时代，从海量、异构且关联复杂的多维数据中提取深层洞察成为重大挑战。大型语言模型（LLM）虽在自然语言理解与生成方面表现优异，但在处理结构化知识时仍存在‘幻觉’问题，且难以实时更新；而知识图谱（KG）虽能显式存储结构化知识，但其静态特性限制了动态交互与分析能力。为此，本文提出一种基于大模型代理与知识图谱交互的多维数据分析方法，构建动态协同分析生态系统。该方法利用大模型代理从非结构化数据中自动提取产品数据，实时构建并可视化知识图谱，通过交互式平台支持用户对图谱节点进行深度探索与分析。实验结果表明，该方法在产品生态分析、关系挖掘及用户驱动的探索性分析方面具有显著优势，为多维数据提供了新的分析思路与工具。
原文摘要 · Abstract (English)
In the current era of big data, extracting deep insights from massive, heterogeneous, and complexly associated multi-dimensional data has become a significant challenge. Large Language Models (LLMs) perform well in natural language understanding and generation, but still suffer from "hallucination" issues when processing structured knowledge and are difficult to update in real-time. Although Knowledge Graphs (KGs) can explicitly store structured knowledge, their static nature limits dynamic interaction and analytical capabilities. Therefore, this paper proposes a multi-dimensional data analysis method based on the interactions between LLM agents and KGs, constructing a dynamic, collaborative analytical ecosystem. This method utilizes LLM agents to automatically extract product data from unstructured data, constructs and visualizes the KG in real-time, and supports users in deep exploration and analysis of graph nodes through an interactive platform. Experimental results show that this method has significant advantages in product ecosystem analysis, relationship mining, and user-driven exploratory analysis, providing new ideas and tools for multi-dimensional data analysis.
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