Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case Study
Zahra Sepasdar, Sushant Gautam, Cise Midoglu, Michael A. Riegler, and, P{\aa}l Halvorsen

TL;DR
Structured-GraphRAG is a graph-based framework that improves retrieval accuracy and efficiency from complex structured datasets, demonstrated through a soccer data case study, by grounding language model responses in knowledge graphs.
Contribution
The paper introduces Structured-GraphRAG, a novel framework that leverages multiple knowledge graphs to enhance data retrieval from structured datasets in natural language queries.
Findings
Significantly improved query processing efficiency.
Reduced response times compared to traditional methods.
Enhanced reliability of language model outputs.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGraph Theory and Algorithms · Data Mining Algorithms and Applications · Data Management and Algorithms
