Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and related applications, which aims to complete the structure of knowledge graph by predicting the missing entities or relationships in knowledge graph and mining unknown facts. Starting from the definition and types of KGC, existing technologies for KGC are analyzed in categories. From the evolving point of view, the KGC technologies could be divided into traditional and representation learning based methods. The former mainly includes rule-based reasoning method, probability graph model, such as Markov logic network, and graph computation based method. The latter further includes translation model based, semantic matching model based, representation learning based and other neural network model based methods. In this paper, different KGC technologies are introduced, including their advantages, disadvantages and applicable fields. Finally the main challenges and problems faced by the KGC are discussed, as well as the potential research directions.

Predicting city-scale taxi origin-destination (OD) flows takes an important role in understanding passengers’ travel demands as well as managing taxi operation and scheduling. But the complex spatial dependencies and temporal dynamics make this problem challenging. In this paper, a hybrid deep neural network prediction model based on convolutional LSTM (ConvLSTM) is proposed. For improving the prediction accuracy, the implicit correlation between travel time and OD flow is explored and they are combined as inputs of the prediction model. Moreover, in order to realize OD flows prediction at the road network level, and solve the problem that grid-based representation method cannot distinguish traffic flow at different heights, such as in multi-layer overpass areas, this paper presents a grid and road nested method to represent ODs. With the time of day partitioned into time slots, OD flows are extracted and predicted in both spatial and temporal domain. In the experiment, real taxi data are used to verify the proposed model and prediction method fully. And the experimental results show that the proposed model can effectively predict city-wide taxi OD flow, and outperforms the typical time sequence models and existing deep neural network models.

The accurate forecasting of urban taxi demands, which is a hot topic in intelligent transportation research, is challenging due to the complicated spatial-temporal dependencies, the dynamic nature, and the uncertainty of traffic. To make full use of the global and local correlations between traffic flows on road sections, this paper presents a deep learning model based on a graph convolutional network, long short-term memory (LSTM), and multitask learning. First, an undirected graph model was formed by considering the spatial pattern distribution of taxi trips on road networks. Then, LSTMs were used to extract the temporal features of traffic flows. Finally, the model was trained using a multitask learning strategy to improve the model’s generalizability. In the experiments, the efficiency and accuracy were verified with real-world taxi trajectory data. The experimental results showed that the model could effectively forecast the short-term taxi demands on the traffic network level and outperform state-of-the-art traffic prediction methods.