TransE, the embedding representation method, is the most classic translation-based method. But it also has two defects. One is the limitation in dealing with complex relations. The other one is Euclidean distance is used as a measure in the scoring function and each feature dimension is calculated with the same weight, so the accuracy will be affected by irrelevant dimensions and the flexibility is lower. Therefore, in view of these two defects, this paper proposed an adaptive KG embedding representation method, namely, TransAD. It replaced the measure function and then introduced a diagonal weight matrix into the score function to assign weights to each feature dimension respectively to increase the representation ability of the model. At the same time, inspired by TransD, it built a spatial projection model of entity and established relationship through dynamic mapping matrix to enhance the processing ability of the model for complex relations. Finally, it integrated the two optimizations into the TransAD model. The experimental results show that TransAD is superior to Trans(E, H, R, D) , and it is advanced in various indexes of link prediction and triad classification tasks and has certain advantages.

[2021-01-05] Printed Article /doc/2019.11.0605/v2