Structured Knowledge Base as Prior Knowledge to Improve Urban Data Analysis

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.

Research on the Construction of a Knowledge Graph and Knowledge Reasoning Model in the Field of Urban Traffic

“…Zhang et al proposed a semantic framework for integrating the Internet of Things with machine learning for smart city applications, and conducted two case studies: Pollution detection from vehicles and traffic pattern detection [17]. They also proposed a method to use structured prior knowledge in the form of knowledge graphs to solve practical problems in urban computing, such as optimal store placement and traffic accident inference [18]. Muppalla et al proposed the imagery-based traffic-sensing knowledge graph framework, which utilizes stationary traffic camera information as sensors to identify dynamic traffic conditions [19].…”

Knowledge-driven Site Selection via Urban Knowledge Graph

“…For example, DeepStore [22] and AR 2 Net [40] extracts features from commercial data, satellite images, etc., and further combine deep neural networks with attention mechanism for solution. UKG-NN [49] builds a relational graph with manually defined features, which are passed to the neural network for site decisions. NeuMF-RS [19] adds restaurants' and sites' attributes to neural collaborative filtering for site selection.…”

Venue-Popularity Prediction Using Social Data Participatory Sensing Systems and RNNs

“…The prediction model consists of multimodal classifiers derived from the deep forest algorithm using three types of extracted features: spatial, mobility, and business features, such as the price and quality of provided services. Besides the existing knowledge graphs such as Wikipedia and ConecptNet5, knowledge-graph convolution neural network (KG-CNN) was proposed in [35] to predict the popularity of venues using features generated from urban knowledge graphs. Table II summarizes the features extracted from datasets other than social data and the models used by different studies.…”

“…[10] Starbucks, Dunkin' Donuts, McDonald [12] Venues with Similar category [25] Multiple categories [13] Restaurants [14] Live Campaigns [11] Starbucks [26] Food business [27] Restaurants, Nightlife, Active Life, Shopping, Hotels, Beauty and Spas, Food, Arts and Entertainment, Religious Organizations and Mass Media [28] Event [29] Billboards [30] Hotels, Shops, Services, Gym and Restaurants. [32,35] Coffee shops, Express inn [33] Retail Shop [34] Hotels…”