title: SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic
    Retrieval
summary: 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.
paper_authors._id: 676b765f038795095f73b556
paper_authors.name: Aakash Mahalingam
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-20T09:30:32.076Z
paper_authors._id: 653fb335165813113370ca70
paper_authors.avatarUrl: /avatars/12a857024e68f150c17162b6958ed30a.svg
paper_authors.fullname: Aakash Mahalingam
paper_authors.type: user
paper_authors.user: ashgam
paper_authors._id: 676b765f038795095f73b557
paper_authors.name: Vinesh Kumar Gande
paper_authors._id: 676b765f038795095f73b558
paper_authors.name: Aman Chadha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-30T19:32:58.838Z
paper_authors._id: 63a4754927f1f64ed7238dac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_authors._id: 676b765f038795095f73b559
paper_authors.name: Vinija Jain
paper_authors._id: 676b765f038795095f73b55a
paper_authors.name: Divya Chaudhary
paper_title: SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic
    Retrieval
paper_summary: 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.
paper_ai_keywords: Retrieval-Augmented Generation (RAG) semantic text retrieval knowledge graphs QuALITY QASPER NarrativeQA Italian Cuisine-SKETCH answer_relevancy faithfulness context_precision context_recall