Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory

Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory

Chenyi Zhuang, Nicholas Jing Yuan, Ruihua Song, Xing Xie, Qiang Ma

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3616-3623. https://doi.org/10.24963/ijcai.2017/506

Technologies are increasingly taking advantage of the explosion in the amount of data generated by social multimedia (e.g., web searches, ad targeting, and urban computing). In this paper, we propose a multi-view learning framework for presenting the construction of a new urban movement knowledge graph, which could greatly facilitate the research domains mentioned above. In particular, by viewing GPS trajectory data from temporal, spatial, and spatiotemporal points of view, we construct a knowledge graph of which nodes and edges are their locations and relations, respectively. On the knowledge graph, both nodes and edges are represented in latent semantic space. We verify its utility by subsequently applying the knowledge graph to predict the extent of user attention (high or low) paid to different locations in a city. Experimental evaluations and analysis of a real-world dataset show significant improvements in comparison to state-of-the-art methods.
Keywords:
Machine Learning: Data Mining
Machine Learning: Relational Learning
Multidisciplinary Topics and Applications: AI and Social Sciences
Machine Learning: Unsupervised Learning