title: Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case
    Study
summary: 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.
paper_authors._id: 66f67233e8dde585ba259a57
paper_authors.name: Zahra Sepasdar
paper_authors._id: 66f67233e8dde585ba259a58
paper_authors.name: Sushant Gautam
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-01T13:19:38.625Z
paper_authors._id: 62c4779e0a06c039315fb57d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62c4779e0a06c039315fb57d/LWdhrmJBGoyCqTFGS5Ap3.jpeg
paper_authors.fullname: Sushant Gautam
paper_authors.type: user
paper_authors.user: SushantGautam
paper_authors._id: 66f67233e8dde585ba259a59
paper_authors.name: Cise Midoglu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-01T13:19:45.217Z
paper_authors._id: 65e0cbf63abb68b76c32f943
paper_authors.avatarUrl: /avatars/e7e43a30c6e678c158dd1eb6bb417720.svg
paper_authors.fullname: Cise Midoglu
paper_authors.type: user
paper_authors.user: cise-midoglu
paper_authors._id: 66f67233e8dde585ba259a5a
paper_authors.name: Michael A. Riegler
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-01T13:19:53.546Z
paper_authors._id: 63b537ee889aa6707f07fa7a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1672825823795-63b537ee889aa6707f07fa7a.png
paper_authors.fullname: Michael Riegler
paper_authors.type: user
paper_authors.user: kelkalot
paper_authors._id: 66f67233e8dde585ba259a5b
paper_authors.name: Pål Halvorsen
paper_title: Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case
    Study
paper_summary: 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.
paper_ai_keywords: Structured-GraphRAG knowledge graphs graph-based approach