{"type":"ScholarlyArticle","title":"Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory","authors":[{"name":"Chenyi Zhuang","slug":"chenyi-zhuang","given":"Chenyi","family":"Zhuang","affiliation":"Department of Informatics , Kyoto University , Kyoto , Japan"},{"name":"Nicholas Jing Yuan","slug":"nicholas-jing-yuan","given":"Nicholas Jing","family":"Yuan","affiliation":"Microsoft Research Asia , Beijing , China"},{"name":"Ruihua Song","slug":"ruihua-song","given":"Ruihua","family":"Song","affiliation":"Microsoft Research Asia , Beijing , China"},{"name":"Xing Xie","slug":"xing-xie","given":"Xing","family":"Xie","affiliation":"Microsoft Research Asia , Beijing , China"},{"name":"Qiang Ma","slug":"qiang-ma","given":"Qiang","family":"Ma","affiliation":"Department of Informatics , Kyoto University , Kyoto , Japan"}],"abstract":"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.","doi":"10.24963/ijcai.2017/506","url":"https://ijcai.org/proceedings/2017/506","pdf":"https://ijcai.org/proceedings/2017/0506.pdf","markdown":"https://ijcai.org/proceedings/2017/506.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/506","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra, IIIA-CSIC","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":506,"pages":{"first":3616,"last":3623},"keywords":["Machine Learning: Unsupervised Learning","Multidisciplinary Topics and Applications: AI and Social Sciences","Machine Learning: Relational Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Bao et al., 2015] Jie Bao, Yu Zheng, David Wilkie, and Mo- hamed Mokbel. 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