Semantic ontology enabled modeling, retrieval and inference for incomplete mobile trajectory data

Abstract

Prevalence of vehicular terminals enables more and more mobile trajectory data to be collected. To promote the intelligent urban planning, traffic management and service in smart city applications, how to analyze and reasoning the inherent patterns in mobile trajectory has grabbed considerable research attentions from academia and industry. Current widely used popular methods are subject to the check-in dataset, and the pattern inference results greatly suffer from the sparsity and incompleteness in mobile trajectory data. To address this issue, a method of semantic ontology enabled modeling, retrieval and inference for incomplete mobile trajectory is investigated in this paper. To enhance the interpretability of mobile trajectory data, the latitude and longitude coordinates in spatio-temporal sequence are converted into semantic locations, and an ontology model with constraints then is constructed for vehicle movement through defining the corresponding semantic rules with the considerations of the location relationships both between road intersections and between vehicle movements. On the basis, by taking the constructed ontology model as the knowledge base and taking the semantic query results as the training set, an ontology based markov logic network is investigated for the mobile trajectory inference problem with incomplete data. Through comprehensive comparison and analysis, the experimental results achieved on real-world data finally have been shown to demonstrate the efficiency of the investigations, and verify the influence of weight configurations on the inference performance achieved by the proposed ontology based markov logic network.