Work

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

Year: 2025

Type: conference-paper

Abstract: 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.

DOI: 10.1145/3722565.3727197

Authors: Ozan Baris Mulayim, Gabe Fierro, Mario Bergés, Marco Pritoni

Institutions: Lawrence Berkeley National Laboratory, Carnegie Mellon University, Colorado School of Mines

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funder: Universitas Brawijaya

Award: G7990950195

OpenAlex ID: W4409798800

FWCI: 5.262

Cites: 19

Cited by: 5

Related to: 10