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Structured Knowledge Base as Prior Knowledge to Improve Urban Data Analysis

Artificial Intelligence and Future Network Technology Research Institute, Zhejiang Lab, Hangzhou 311121, China
Colledge of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
Alibaba-Zhejiang University Joint Institue of Frontier Technologies, AIBABA, Hangzhou 310058, China
Department of Computer Science, Oxford University, Oxford OX1 3QR, UK
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(7), 264;
Received: 14 May 2018 / Revised: 27 June 2018 / Accepted: 3 July 2018 / Published: 7 July 2018
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
PDF [2643 KB, uploaded 7 July 2018]


Urban computing at present often relies on a large number of manually extracted features. This may require a considerable amount of feature engineering, and the procedure may miss certain hidden features and relationships among data items. In this paper, we propose a method to use structured prior knowledge in the form of knowledge graphs to improve the precision and interpretability in applications such as optimal store placement and traffic accident inference. Specifically, we integrate sub-graph feature extraction, sub-knowledge graph gated neural networks, and kernel-based knowledge graph convolutional neural networks as ways of incorporating large urban knowledge graphs into a fully end-to-end learning system. Experiments using data from several large cities showed that our method outperforms the baseline methods. View Full-Text
Keywords: internet of things; smart city; energy management; traffic pattern internet of things; smart city; energy management; traffic pattern

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Zhang, N.; Deng, S.; Chen, H.; Chen, X.; Chen, J.; Li, X.; Zhang, Y. Structured Knowledge Base as Prior Knowledge to Improve Urban Data Analysis. ISPRS Int. J. Geo-Inf. 2018, 7, 264.

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