Multi-dimensional Data Analysis and Applications Basing on LLM Agents and Knowledge Graph Interactions
This work addresses the problem of analyzing heterogeneous and associated data for users in data-intensive fields, representing an incremental improvement by integrating existing technologies.
The paper tackles the challenge of extracting insights from complex multi-dimensional data by proposing a method that combines LLM agents and Knowledge Graphs to create a dynamic analytical ecosystem, showing significant advantages in product ecosystem analysis, relationship mining, and user-driven exploratory analysis.
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.