利用GraphRAG增強結構化數據檢索：足球數據案例研究

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

從大型和複雜數據集中提取有意義的見解存在著重大挑戰，特別是在確保檢索信息的準確性和相關性方面。傳統的數據檢索方法，如順序搜索和基於索引的檢索，在處理錯綜複雜的數據結構時往往失敗，導致輸出不完整或具有誤導性。為了克服這些限制，我們引入了Structured-GraphRAG，這是一個多功能框架，旨在增強自然語言查詢中結構化數據集的信息檢索。Structured-GraphRAG利用多個知識圖，這些圖以結構化格式表示數據並捕捉實體之間的複雜關係，從而實現更加細緻和全面的信息檢索。這種基於圖的方法通過將回應基於結構化格式來減少語言模型輸出中的錯誤風險，從而增強結果的可靠性。我們通過將其性能與最近發表的使用傳統檢索增強生成的方法進行比較，展示了Structured-GraphRAG的有效性。我們的研究結果表明，Structured-GraphRAG顯著提高了查詢處理效率並降低了響應時間。雖然我們的案例研究聚焦於足球數據，但該框架的設計具有廣泛的應用價值，為數據分析提供了一個強大工具，並增強了語言模型在各種結構化領域中的應用。

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.