Abstract：The existing temporal knowledge graph representation methods cannot capture the complex relationships within quadruple well. Most of the neural network based models are unable to model time-varying knowledge and capture rich feature information. Moreover, the interaction between entities and relations in these models is poor. Therefore, a multi-scale dilated convolutional neural network model based on attention mechanism(MSDCA) is proposed. Firstly, a time-aware relation representation is obtained using long short-term memory. Secondly, a multi-scale dilated convolutional neural network is employed to improve the interactivity of the quadruple. Finally, a multi-scale attention mechanism is utilized to capture critical features to improve completion ability of MSDCA. Link prediction experiments on multiple public temporal datasets show the superiority of MSDCA.
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