Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction

With the rapid development of mobile communication technology, trajectories humans are massively collected by Internet service providers (ISPs) and application (ASPs). On other hand, rising paradigm knowledge graph (KG) provides us a promising solution to extract structured "knowledge" from massive trajectory data. In this paper, we focus on modeling users' spatio-temporal mobility patterns based techniques, predicting future movement extracted multiple sources in cohesive manner. Specifically, propose new type graph, i.e., urban (STKG), where trajectories, category information venues, temporal jointly modeled facts with different relation types STKG. The prediction problem is converted completion Further, complex embedding model elaborately designed scoring functions proposed measure plausibility STKG solve problem, which considers dynamics utilizes PoI categories as auxiliary background knowledge. Extensive evaluations confirm high accuracy our mobility, improving 5.04% compared state-of-the-art algorithms. addition, confirmed be helpful performance 3.85% terms accuracy. Additionally, experiments show that method time-efficient reducing computational time over 43.12% existing methods.