Urban Knowledge Graph
Description
The development of smart cities relies on the growing research in data-driven urban intelligence, driven by the tremendous accumulation in urban data and advancements in artificial intelligence. However, current research faces issues of robustness and explainability. To overcome these obstacles and foster the growth of smart cities, the emergence of robust and explainable urban knowledge is important. Building urban knowledge graph (UrbanKG) offers a tailored solution for urban environments. This comprehensive book serves as a roadmap to UrbanKG, beginning with a foundational understanding of knowledge graphs and delving into the methods of constructing UrbanKG from diverse urban data sources. It explains methodologies for learning representations of UrbanKG, enabling the extraction of semantic and structural information. Furthermore, it explores a range of UrbanKG applications, including urban mobility, user behavior modelling, recommender systems, and mobile networks. Finally, it concludes and discusses future directions of UrbanKG. This book caters to a broad audience, including students, researchers, and professionals in fields such as urban computing, machine learning, and data mining. By offering both theoretical insights and practical applications, it not only enriches understanding but also presents a potential solution to challenges in the landscape of smart cities.
More details
Persons
Prof . Yong Li is a full Professor in the Department of Electronic Engineering at Tsinghua University. Focusing on artificial intelligence, data science, and interdisciplinary research, he has published in comprehensive journals such as Nature , Nature Machine Intelligence , Nature Computational Science , Nature Human Behaviors , and Nature Cities , and he has authored more than 200 academic papers in CCF-A ranked conference and journals, including ACM NeurIPS, KDD, ICLR, and WWW, with over 40,000 citations and more than 100 authorized patents.
He has been recognized as a Global Highly Cited Researcher, and received seven Best Paper or Outstanding Paper Awards at prestigious international conferences in computer science, including ACL, WWW, SIGIR, and UbiComp. He has served more than 30 times as a Senior Program Committee or Program Committee member for international conferences such as KDD, WWW, AAAI, and IJCAI, and as an editorial board member or guest editor for international journals like ACM IMWUT , IEEE JSAC , IEEE TNSM , and IEEE CM .
Yu Liu is a postdoctoral researcher in the Department of Engineering Science at the University of Oxford. He received his Ph.D. degree with honors in 2023 and his B.Eng. degree in 2018, both from the Department of Electronic Engineering at Tsinghua University. His research interests include data mining, knowledge graphs, foundation models, multimodal learning, and trustworthy AI, with applications in healthcare, biomedicine, urban computing, and sustainable development.
Zhilun Zhou is a Ph.D. candidate in the Department of Electronic Engineering at Tsinghua University. He received his B.S. degree from Tsinghua University in 2022. His research interests focus on urban computing and large language models, including urban knowledge graphs, socioeconomic prediction, urban planning, and LLM-based multi-agent systems.
Content
Chapter 1. Introduction.- Chapter 2. Construction of Urban Knowledge Graph.- Chapter 3. Representation of Urban Knowledge Graph.- Chapter 4. Applications in Urban Computing.- Chapter 5. Applications in User Behavior.- Chapter 6. Applications in Recommender System.- Chapter 7. Applications in Mobile Network.- Chapter 8. Conclusions.