Completion of Temporal Knowledge Graph for Historical Contrastive Learning

Aiming at the problem that the existing temporal knowledge graph completion model is highly dependent on events that have occurred in history and the prediction of events that have not occurred in history is inaccurate, a completion of temporal knowledge graph for comparing historical and non-historical information (CHNH) with time series information is proposed. Firstly, the model captures long-term dependencies in the sequence through BiLSTM, ensuring accurate encoding of historical information. Then, the graph convolution operation is performed using RGCN to learn the global graph representation. In the prediction process, different scoring functions are used for separately coded historical and non-historical information to determine the dependence degree of the prediction entity on these two types of information. In this way, the model can more effectively complete entities and relationships, improving the predictive performance of the model. Experimental results on ICEWS18, GDERT and YAGO datasets show that the CHNH model generally outperforms the baseline model in MRR, Hits@1, Hits@3 and Hits@10. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.