A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
Table 3: Hits@10 on the WN18RR validation set with N=50 and the initial learning rate at 1e−5 w.r.t each number of iterations in the routing algorithm m and each 10 training epochs.
['[ITALIC] m', '10', '20', '30', '40', '50']
Effects of routing iterations: We study how the number of routing iterations affect the performance. w.r.t each number value of the routing iterations and epochs with the number of filters N=50 and the Adam initial learning rate at 1e−5. We see that the best performance for each setup over each 10 epochs is obtained by setting the number m of routing iterations to 1. This indicates the opposite side for knowledge graphs compared to images. In the image classification task, setting the number m of iterations in the routing process higher than 1 helps to capture the relative positions of entities in an image (e.g., eyes, nose and mouth) properly. In contrast, this property from images may be only right for the 1-1 relations, but not for the 1-M, M-1 and M-M relations in the KGs because of the high variant of each relation type (e.g., symmetric relations) among different entities.