The effective extraction and structuring of knowledge from technical documentation remains a significant challenge in both educational and industrial contexts. Traditional information extraction approaches show limitations in capturing complex relationships and domain-specific terminology present in procedural manuals. This paper presents a novel intelligent framework that combines Large Language Models with ontological guidance for automatic extraction of entities and semantic relationships from educational manuals. The system integrates preprocessing and indexing of manuals, knowledge extraction based on Retrieval-Augmented Generation with ontological constraints, and conversational interaction for real-time procedural guidance. Evaluation across ten diverse scientific manuals demonstrated strong performance in tasklist extraction, question-answering, and action validation tasks.