Research on the Application of Neural Network Knowledge graph Model in Machine translation Knowledge graph Reasoning
The Knowledge graph stores the relationship between entities and entity information in a structured way,which can process information quickly and efficiently,but it has shortcomings such as sparsity and incompleteness.The study first constructs a High Efficient Transformer(HET)by designing reversible residual modules,input modules,and output modules.Then,it utilizes the high accuracy and interpretability of FOL rules to complete knowledge inference,forming a mixed logic rule and neural network knowledge inference model(HETIL).The results show that with the increase of the number of steps,the lowest Perplexity of HET ef-ficient neural network model is only 3.6.In the case of English Machine translation,the Mean reciprocal rank(MRR)index of HETIL hybrid reasoning model for web page English translation scenarios can reach 0.67,and the minimum time consumption is only 18 seconds.It shows that the proposed model has high running efficiency in Machine translation and can effectively complete reason-ing tasks,which provides new ideas and methods for the development of English Machine translation.
万方数据
维普
