Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case
  Study
Authors: name: Zahra Sepasdar, name: Sushant Gautam, name: Cise Midoglu, name: Michael A. Riegler, name: Pål Halvorsen

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.