LLMasMMKG: LLM Assisted Synthetic Multi-Modal Knowledge Graph Creation For Smart City Cognitive Digital Twins

Abstract

The concept of a Smart City (SC) Cognitive Digital Twin (CDT) presents significant potential for optimizing urban environments through sophisticated simulations, predictions, and informed decision-making. Comprehensive Knowledge Representations (KRs) that effectively integrate the diverse data streams generated by a city are crucial to realizing this potential. This paper addresses this by introducing a novel approach that leverages Large Language Models (LLMs) to automate the construction of synthetic Multi-Modal (MM) Knowledge Graphs (KGs) specifically designed for a SC CDT. Recognizing the challenges in fusing and aligning information from disparate sources, our method harnesses the power of LLMs for natural language understanding, entity recognition, and relationship extraction to seamlessly integrate data from sensor networks, social media feeds, official reports, and other relevant sources. Furthermore, we explore the use of LLM-driven synthetic data generation to address data sparsity issues, leading to more comprehensive and robust KGs. Initial outputs demonstrate the effectiveness of our approach in constructing semantically rich and interconnected synthetic KGs, highlighting the significant potential of LLMs for advancing SC CDT technology.