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-2-13B, we obtain the UrbanKGC
agent, UrbanKGent-13B. We perform a comprehensive evaluation on two real-world
datasets using both human and GPT-4 self-evaluation. The experimental results
demonstrate that UrbanKGent-13B not only can significantly outperform 21
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. We deploy
UrbanKGent-13B to provide online services, which can construct an UrbanKG with
thousands of times richer relationships using only one-fifth of the data
compared with the existing benchmark. Our data, code, and opensource UrbanKGC
agent are available at this https URL

这篇论文介绍了 UrbanKGent，一个用于城市知识图谱构建的统一大型语言模型代理框架，通过异构感知的指令生成构建了 UrbanKGC 任务的知识指令集，并提出了一个工具增强的迭代轨迹优化模块。通过在 Llama-2-13B 上进行混合指令微调，得到了 UrbanKGC 代理 UrbanKGent-13B。通过实验评估表明，UrbanKGent-13B 在 UrbanKGC 任务中不仅能显著优于 21 个对比方法，还在低成本的情况下超过了当前最先进的 LLM 和 GPT-4 方法。部署 UrbanKGent-13B 提供在线服务，可通过仅使用现有基准的五分之一的数据，构建出具有数千倍以上关系的 UrbanKG。

UrbanKGent: 城市知识图谱构建的统一大型语言模型代理框架

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

Accurate Urban SpatioTemporal Prediction (USTP) is of great importance to the
development and operation of the smart city. As an emerging building block,
multi-sourced urban data are usually integrated as urban knowledge graphs
(UrbanKGs) to provide critical knowledge for urban spatiotemporal prediction
models. However, existing UrbanKGs are often tailored for specific downstream
prediction tasks and are not publicly available, which limits the potential
advancement. This paper presents UUKG, the unified urban knowledge graph
dataset for knowledge-enhanced urban spatiotemporal predictions. Specifically,
we first construct UrbanKGs consisting of millions of triplets for two
metropolises by connecting heterogeneous urban entities such as administrative
boroughs, POIs, and road segments. Moreover, we conduct qualitative and
quantitative analysis on constructed UrbanKGs and uncover diverse high-order
structural patterns, such as hierarchies and cycles, that can be leveraged to
benefit downstream USTP tasks. To validate and facilitate the use of UrbanKGs,
we implement and evaluate 15 KG embedding methods on the KG completion task and
integrate the learned KG embeddings into 9 spatiotemporal models for five
different USTP tasks. The extensive experimental results not only provide
benchmarks of knowledge-enhanced USTP models under different task settings but
also highlight the potential of state-of-the-art high-order structure-aware
UrbanKG embedding methods. We hope the proposed UUKG fosters research on urban
knowledge graphs and broad smart city applications. The dataset and source code
are available at this https URL

本文介绍了 UUKG，即用于知识增强的城市时空预测的统一城市知识图谱数据集。通过构建 UrbanKGs 并分析其中的高阶结构模式，文章实现了对城市空间时间预测的知识增强，并展示了知识增强模型与不同任务场景下城市知识图谱嵌入方法的潜在联系，其公开的数据集和源代码可在此 https URL 获取。

UUKG：面向城市时空预测的统一城市知识图谱数据集

UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal  Prediction

With the rapid development of the mobile communication technology, mobile
trajectories of humans are massively collected by Internet service providers
(ISPs) and application service providers (ASPs). On the other hand, the rising
paradigm of knowledge graph (KG) provides us a promising solution to extract
structured "knowledge" from massive trajectory data. In this paper, we focus on
modeling users' spatio-temporal mobility patterns based on knowledge graph
techniques, and predicting users' future movement based on the "knowledge''
extracted from multiple sources in a cohesive manner. Specifically, we propose
a new type of knowledge graph, i.e., spatio-temporal urban knowledge graph
(STKG), where mobility trajectories, category information of venues, and
temporal information are jointly modeled by the facts with different relation
types in STKG. The mobility prediction problem is converted to the knowledge
graph completion problem in STKG. Further, a complex embedding model with
elaborately designed scoring functions is proposed to measure the plausibility
of facts in STKG to solve the knowledge graph completion problem, which
considers temporal dynamics of the mobility patterns and utilizes PoI
categories as the auxiliary information and background knowledge. Extensive
evaluations confirm the high accuracy of our model in predicting users'
mobility, i.e., improving the accuracy by 5.04% compared with the
state-of-the-art algorithms. In addition, PoI categories as the background
knowledge and auxiliary information are confirmed to be helpful by improving
the performance by 3.85% in terms of accuracy. Additionally, experiments show
that our proposed method is time-efficient by reducing the computational time
by over 43.12% compared with existing methods.

本文利用空间 — 时间城市知识图谱中的移动轨迹，特定地点类别信息及时间信息，设计了一个模型，旨在解决基于知识图谱技术的用户的远期移动预测问题，提高了预测精确度。同时，对 POI 类别作为背景知识和辅助信息的实验结果表明，可帮助提升预测精度。