利用GraphRAG增强结构化数据检索：足球数据案例研究

Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case Study

September 26, 2024

作者: Zahra Sepasdar, Sushant Gautam, Cise Midoglu, Michael A. Riegler, Pål Halvorsen

cs.AI

摘要

从大型和复杂数据集中提取有意义的见解面临着重大挑战，特别是在确保检索信息的准确性和相关性方面。传统的数据检索方法，如顺序搜索和基于索引的检索，在处理复杂和相互关联的数据结构时经常失败，导致输出不完整或误导性。为了克服这些局限性，我们引入了Structured-GraphRAG，这是一个多功能框架，旨在增强自然语言查询中对结构化数据集的信息检索。Structured-GraphRAG利用多个知识图，这些图以结构化格式表示数据并捕获实体之间的复杂关系，从而实现更加细致和全面的信息检索。这种基于图的方法通过将响应基于结构化格式，降低了语言模型输出错误的风险，从而提高了结果的可靠性。我们通过将其性能与最近发表的一种使用传统检索增强生成的方法进行比较，展示了Structured-GraphRAG的有效性。我们的研究结果显示，Structured-GraphRAG显著提高了查询处理效率并减少了响应时间。虽然我们的案例研究集中在足球数据上，但该框架的设计具有广泛适用性，为数据分析提供了强大工具，并增强了各种结构化领域中语言模型应用的能力。

English

Extracting meaningful insights from large and complex datasets poses significant challenges, particularly in ensuring the accuracy and relevance of retrieved information. Traditional data retrieval methods such as sequential search and index-based retrieval often fail when handling intricate and interconnected data structures, resulting in incomplete or misleading outputs. To overcome these limitations, we introduce Structured-GraphRAG, a versatile framework designed to enhance information retrieval across structured datasets in natural language queries. Structured-GraphRAG utilizes multiple knowledge graphs, which represent data in a structured format and capture complex relationships between entities, enabling a more nuanced and comprehensive retrieval of information. This graph-based approach reduces the risk of errors in language model outputs by grounding responses in a structured format, thereby enhancing the reliability of results. We demonstrate the effectiveness of Structured-GraphRAG by comparing its performance with that of a recently published method using traditional retrieval-augmented generation. Our findings show that Structured-GraphRAG significantly improves query processing efficiency and reduces response times. While our case study focuses on soccer data, the framework's design is broadly applicable, offering a powerful tool for data analysis and enhancing language model applications across various structured domains.