Abstract
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.
Highlights
Nowadays, applications of urban computing often rely on manual feature engineering tasks, which may lead to some latent features being overlooked
We propose an urban knowledge graph neural network (UKG-NN) that uses features from raw urban data and graphical features extracted from an urban KG
UKG-NN employs convolution-based neural network by considering global, propagation-specific, and locale-specific features automatically generated from an urban knowledge graph as well as manually extracted features from raw urban data
Summary
Applications of urban computing often rely on manual feature engineering tasks, which may lead to some latent features being overlooked. It is usually necessary to construct and combine some complex features for machine learning tasks in urban computing. The complexity of applications and the different modalities of urban data make the feature construction task extremely challenging. Data generated from sensors and social media in cities contain millions of concepts that are understood by humans. Each region in a large city contains some hidden and inherent knowledge (e.g., demographics, points of interest, and so on). Modern learning-based approaches usually require thousands of labeled instances with complicated feature engineering. A combination of prior urban knowledge and available approaches are used for this
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