Towards Zero-shot Question Answering in CPS-IoT: Large Language Models and Knowledge Graphs

Natural language provides an intuitive interface for querying data, yet its unstructured nature often makes precise retrieval of information challenging. Knowledge graphs (KGs), with their structured and relational representations, offer a powerful solution to structuring knowledge, while large language models (LLMs) are capable of interpreting user intent through language. This combination of KGs and LLMs has been explored extensively for Knowledge Graph Question Answering (KGQA), primarily for open-domain or encyclopedic knowledge. Domain-specific KGQA, instead, presents significant opportunities for Cyber-Physical Systems (CPS) and the Internet of Things (IoT), where the extraction of structured metadata is essential for automation and scalability of control and analytics applications.