ChatTf: A Knowledge Graph-Enhanced Intelligent Q\&A System for Mitigating Factuality Hallucinations in Traditional Folklore

Large language models are rapidly advancing the field of artificial intelligence, with current research focusing primarily on traditional natural language understanding tasks, such as question answering and information extraction. However, in knowledge-intensive domains, such as intangible cultural heritage, hallucination problems due to insufficient domain knowledge persist. To address this, we present ChatTf, a knowledge graph-enhanced intelligent Q\&A system, exemplified by Chinese traditional folklore, aimed at reducing factuality hallucinations in this domain. Specifically, we constructed the Traditional Folklore Ontology (TFOnto) and proposed the Zero-shot Traditional Folklore Triplet Extraction (ZFTE) framework. Driven by TFOnto, ZFTE builds a Traditional Folklore Knowledge Graph (TFKG). We then proposed a dual-stage Retrieval-Augmented Generation framework (TFKG-RAG) based on TFKG to provide traditional folklore knowledge to large language models, mitigating factuality hallucinations in folklore Q\&A tasks. In the experimental phase, ChatTf achieved an accuracy of 96.7\% on a self-built TFCQD test set, outperforming several state-of-the-art baseline methods. This demonstrates the accuracy and reliability of folklore domain question answering.