Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case
  Study
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.
66f67233e8dde585ba259a57
Zahra Sepasdar
66f67233e8dde585ba259a58
Sushant Gautam
admin_assigned
2024-10-01T13:19:38.625Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/62c4779e0a06c039315fb57d/LWdhrmJBGoyCqTFGS5Ap3.jpeg
Sushant Gautam
user
SushantGautam
66f67233e8dde585ba259a59
Cise Midoglu
admin_assigned
2024-10-01T13:19:45.217Z
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Cise Midoglu
user
cise-midoglu
66f67233e8dde585ba259a5a
Michael A. Riegler
admin_assigned
2024-10-01T13:19:53.546Z
63b537ee889aa6707f07fa7a
https://cdn-avatars.huggingface.co/v1/production/uploads/1672825823795-63b537ee889aa6707f07fa7a.png
Michael Riegler
user
kelkalot
66f67233e8dde585ba259a5b
Pål Halvorsen
Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case
  Study
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.
Structured-GraphRAG, a framework using multiple knowledge graphs, enhances information retrieval in natural language queries, improving efficiency and reducing response times over traditional methods.
knowledge graphs structured datasets natural language queries information retrieval graph-based approach language model outputs retrieval-augmented generation