Hierarchical knowledge graph-based QA systems with retrieval-augmented generation

Hierarchical knowledge graphs (KGs) are vital to question-answering (QA) systems for complex queries, integrating structured and unstructured knowledge. This study introduces a QA system combining a hierarchical KG, graph convolutional networks (GCNs), and retrieval-augmented generation (RAG) to enhance reasoning, retrieval, and response generation. The KG organises information into title, subtitle, and content layers for structured, efficient retrieval; GCNs aggregate local and global relations across layers; RAG incorporates external sources (e.g., Wikipedia) for contextually accurate answers. On standard benchmarks, the system outperformed strong baselines in precision, recall, and F1-score, offering an effective solution for complex queries and advancing QA design.