Enhancing Knowledge Graphs with Large Language Models: Contributions to E-commerce Question Answering Systems - Regino, André Gomes; Reis, Julio Cesar dos (Anais Estendidos do Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia); 2025: Anais Estendidos do XXXI Simpósio Brasileiro de Sistemas Multimídia e Web, 2025)
E-commerce platforms demand structured and dynamic knowledge to deliver accurate, humanized, and scalable customer support. This Ph.D. Thesis introduces QART, a novel framework that integrates Large Language Models (LLMs) to automatically extract high-quality RDF triples from question-answer (Q&A) dialogues, enabling population of domain-specific Knowledge Graphs. The method combines semantic summarization, few-shot triple generation, and automated consistency validation to align outputs with existing ontologies. Experimental results across multiple LLMs demonstrate up to 20% gains in precision over baseline approaches and high accuracy in semantic validation tasks (F1 = 0.98). The solution has been validated in an industrial environment, showing potential to reduce human effort, enhance recommendation quality, and improve customer satisfaction through more informed and context-aware answers.