Application of retrieval-augmented generation based on knowledge graphs for building a question-answering system
Abstract
This work examines methods for integrating knowledge graphs into Retrieval-Augmented Generation (RAG) systems to improve the quality of responses to questions that require reasoning over entity relationships and holistic text analysis. The scientific novelty lies in the comparative evaluation of knowledge graph integration strategies versus classical RAG, using the scikit-learn technical documentation as a case study and employing the GraphRAG and RAGAS metrics. The primary objective of the research is to conduct a comparative analysis of the effectiveness of approaches on a specialized dataset and to develop a question-answering system based on the most effective method.
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