Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory
Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory
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DOI：
10.24963/ijcai.2017/506
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
Chenyi Zhuang;Nicholas Jing Yuan;Ruihua Song;Xing Xie;Qiang Ma
中科院分区：
文献类型：
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作者：
Chenyi Zhuang;Nicholas Jing Yuan;Ruihua Song;Xing Xie;Qiang Ma
技术越来越多地利用社交多媒体（例如，网络搜索、广告定位和城市计算）。在本文中，我们提出了一个多视图的学习框架，提出了一个新的城市运动知识图的建设，这可以大大促进上述研究领域。特别是，通过查看GPS轨迹数据从时间，空间和时空的观点，我们构建了一个知识图，其中节点和边缘是它们的位置和关系，分别。在知识图上，节点和边都表示在潜在语义空间中。随后，我们通过应用知识图来预测用户对城市中不同位置的关注程度（高或低）来验证其实用性。对真实世界数据集的实验评估和分析表明，与最先进的方法相比，该方法有显着的改进。
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.