UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction

• Main Authors: Ning, Yansong, Liu, Hao
• Format: Journal Article
• Language: English
• Published: 09-02-2024
• Subjects: Computer Science - Artificial Intelligence
• DOI: 10.48550/arxiv.2402.06861

Urban knowledge graph has recently worked as an emerging building block to
distill critical knowledge from multi-sourced urban data for diverse urban
application scenarios. Despite its promising benefits, urban knowledge graph
construction (UrbanKGC) still heavily relies on manual effort, hindering its
potential advancement. This paper presents UrbanKGent, a unified large language
model agent framework, for urban knowledge graph construction. Specifically, we
first construct the knowledgeable instruction set for UrbanKGC tasks (such as
relational triplet extraction and knowledge graph completion) via
heterogeneity-aware and geospatial-infused instruction generation. Moreover, we
propose a tool-augmented iterative trajectory refinement module to enhance and
refine the trajectories distilled from GPT-4. Through hybrid instruction
fine-tuning with augmented trajectories on Llama 2 and Llama 3 family, we
obtain UrbanKGC agent family, consisting of UrbanKGent-7/8/13B version. We
perform a comprehensive evaluation on two real-world datasets using both human
and GPT-4 self-evaluation. The experimental results demonstrate that UrbanKGent
family can not only significantly outperform 31 baselines in UrbanKGC tasks,
but also surpass the state-of-the-art LLM, GPT-4, by more than 10% with
approximately 20 times lower cost. Compared with the existing benchmark, the
UrbanKGent family could help construct an UrbanKG with hundreds of times richer
relationships using only one-fifth of the data. Our data and code are available
at https://github.com/usail-hkust/UrbanKGent.