SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic
  Retrieval
Retrieval-Augmented Generation (RAG) systems have become pivotal in
leveraging vast corpora to generate informed and contextually relevant
responses, notably reducing hallucinations in Large Language Models. Despite
significant advancements, these systems struggle to efficiently process and
retrieve information from large datasets while maintaining a comprehensive
understanding of the context. This paper introduces SKETCH, a novel methodology
that enhances the RAG retrieval process by integrating semantic text retrieval
with knowledge graphs, thereby merging structured and unstructured data for a
more holistic comprehension. SKETCH, demonstrates substantial improvements in
retrieval performance and maintains superior context integrity compared to
traditional methods. Evaluated across four diverse datasets: QuALITY, QASPER,
NarrativeQA, and Italian Cuisine-SKETCH consistently outperforms baseline
approaches on key RAGAS metrics such as answer_relevancy, faithfulness,
context_precision and context_recall. Notably, on the Italian Cuisine dataset,
SKETCH achieved an answer relevancy of 0.94 and a context precision of 0.99,
representing the highest performance across all evaluated metrics. These
results highlight SKETCH's capability in delivering more accurate and
contextually relevant responses, setting new benchmarks for future retrieval
systems.
676b765f038795095f73b556
Aakash Mahalingam
claimed_verified
2025-01-20T09:30:32.076Z
653fb335165813113370ca70
/avatars/12a857024e68f150c17162b6958ed30a.svg
Aakash Mahalingam
user
ashgam
676b765f038795095f73b557
Vinesh Kumar Gande
claimed_verified
2025-04-02T08:52:17.146Z
6615ab6744425d16e34b7a40
/avatars/7bb7533f6484fd5741dc8554cc13940e.svg
Vinesh
user
vineshgvk
676b765f038795095f73b558
Aman Chadha
claimed_verified
2024-12-30T19:32:58.838Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
676b765f038795095f73b559
Vinija Jain
676b765f038795095f73b55a
Divya Chaudhary
SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic
  Retrieval
Retrieval-Augmented Generation (RAG) systems have become pivotal in
leveraging vast corpora to generate informed and contextually relevant
responses, notably reducing hallucinations in Large Language Models. Despite
significant advancements, these systems struggle to efficiently process and
retrieve information from large datasets while maintaining a comprehensive
understanding of the context. This paper introduces SKETCH, a novel methodology
that enhances the RAG retrieval process by integrating semantic text retrieval
with knowledge graphs, thereby merging structured and unstructured data for a
more holistic comprehension. SKETCH, demonstrates substantial improvements in
retrieval performance and maintains superior context integrity compared to
traditional methods. Evaluated across four diverse datasets: QuALITY, QASPER,
NarrativeQA, and Italian Cuisine-SKETCH consistently outperforms baseline
approaches on key RAGAS metrics such as answer_relevancy, faithfulness,
context_precision and context_recall. Notably, on the Italian Cuisine dataset,
SKETCH achieved an answer relevancy of 0.94 and a context precision of 0.99,
representing the highest performance across all evaluated metrics. These
results highlight SKETCH's capability in delivering more accurate and
contextually relevant responses, setting new benchmarks for future retrieval
systems.
SKETCH enhances RAG systems by integrating semantic text retrieval with knowledge graphs, improving retrieval performance and context integrity across various datasets.
Retrieval-Augmented Generation semantic text retrieval knowledge graphs RAGAS answer_relevancy faithfulness context_precision context_recall