A RAG-Powered Academic Chatbot with Ontology-Driven Factual Verification - do Amaral, Vitor Mateus R.; Silveira, Luiza Espinosa; Rigo, Paula D.; Lunardi, Gabriel M. (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), 2025)
This paper presents an academic chatbot developed to support services at UFSM, with a Retrieval-Augmented Generation (RAG) architecture. The system uses PyPDF2 for processing official documents, LangChain and FAISS for vector retrieval, and Zephyr 7B for response generation. To ensure factual accuracy, our ontology enables automated fact-checking via SPARQL queries. The chatbot is integrated into Telegram with structured conversation flows tailored for Computer Engineering students as case study. Experimental results show gains in factual accuracy through ontology-based verification, reducing hallucinations and increasing system reliability.