SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic
  Retrieval
Authors: name: Aakash Mahalingam, name: Vinesh Kumar Gande, name: Aman Chadha, name: Vinija Jain, name: Divya Chaudhary

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.