Decision-making is a critical process that shapes outcomes in both organizational and personal contexts, influencing how tasks are prioritized, strategies are developed, and problems are resolved. A key factor in effective decision-making is the successful transfer of tacit knowledge—implicit, experience-based insights that are often difficult to articulate but essential for providing context-aware and nuanced perspectives. Chatbots, powered by advanced Natural Language Processing (NLP) technologies, have significant potential to facilitate this knowledge transfer by delivering timely and relevant insights. This paper introduces a novel framework for question modeling using structured ontologies to enhance chatbot interactions across various domains. By dynamically generating context-specific questions, chatbots can engage users in meaningful exchanges that uncover and leverage tacit knowledge. These question sets, systematically stored and analyzed, enable chatbots to adapt and refine their responses, ultimately supporting informed and effective decision-making through enhanced NLP-driven interactions.