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