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Open AccessFeature PaperArticle

Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding

1
School of Geosciences and Info-Physics, Central South University, Changsha 410012, China
2
China Academy of Electronic and Information Technology, Beijing 100086, China
3
School of Architecture, Changsha University of Science and Technology, Changsha 610059, China
4
China Telecom Shanghai Ideal Information Industry (Group) Co., Ltd., Shanghai 200120, China
5
Shanghai Aerospace Control Technology Institute, Shanghai 201109, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(2), 199; https://doi.org/10.3390/sym12020199
Received: 2 January 2020 / Revised: 10 January 2020 / Accepted: 13 January 2020 / Published: 1 February 2020
Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.
Keywords: multi-source spatio-temporal data; knowledge graph; embedded learning; data analysis multi-source spatio-temporal data; knowledge graph; embedded learning; data analysis
MDPI and ACS Style

Zhao, L.; Deng, H.; Qiu, L.; Li, S.; Hou, Z.; Sun, H.; Chen, Y. Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding. Symmetry 2020, 12, 199.

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