Hidden Danger Prediction of Logistics Enterprise Based on Incremental Learning of Knowledge Graph
In view of the multiple correlation relationships of hidden dangers and the time sequence change char-acteristics of hidden dangers facts,the incremental learning method of time sequence knowledge graph is adopted to complete the hidden dangers prediction in specific fields for hidden dangers of logistics enterprises.On the one hand,through the triplet relationship under different time Windows,the multi-class data related to logistics hazards are con-nected to form a structured relationship network,making full use of the existing data facts.On the other hand,consid-ering the temporal variation of facts,the time information is fused by the embedded expression of entity and time.At the same time,after completing the basic model training,the method of incremental learning combined with positive and negative sample training is adopted to reduce resource loss.Experimental results show that the proposed algorithm can greatly improve the prediction efficiency of the model on the basis of guaranteeing the accuracy of prediction.
Incremental learningHidden danger predictionKnowledge graphFeature of time sequence
NETL
NSTL
万方数据
