Hierarchical Knowledge Graph-Based QA System with Retrieval-Augmented Generation
學年 114
學期 1
出版（發表）日期 2025-11-19
作品名稱 Hierarchical Knowledge Graph-Based QA System with Retrieval-Augmented Generation
作品名稱（其他語言）
著者 Wan-Chi Yang; Xuan Li; Chih-Yung Chang; Diptendu Sinha Roy
單位
出版者
著錄名稱、卷期、頁數 Enterprise Information Systems 20(1), p. 1-30
摘要 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.
關鍵字 Information and knowledge management models; hierarchical knowledge graphs; question-answering systems; retrieval-augmented generation
語言 en
ISSN
期刊性質 國外
收錄於 SCI Scopus
產學合作
通訊作者
審稿制度 是
國別 USA
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128590 )

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