A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
Table 2: Experimental results on the WN18RR and FB15k-237 test sets. Hits@10 (H@10) is reported in %. Results of DISTMULT, ComplEx and ConvE are taken from Dettmers et al. (2018). Results of TransE on FB15k-237 are taken from Nguyen et al. (2018). Our CapsE Hits@1 scores are 33.7% on WN18RR and 48.9% on FB15k-237. Formulas of MRR and Hits@1 show a strong correlation, so using Hits@1 does not really reveal any additional information for this task. The best score is in bold, while the second best score is in underline. ⋆ denotes our new results for TransE and ConvKB, which are better than those published by Nguyen et al. (2018).
['[BOLD] Method', '[BOLD] WN18RR MR', '[BOLD] WN18RR MRR', '[BOLD] WN18RR H@10', '[BOLD] FB15k-237 MR', '[BOLD] FB15k-237 MRR', '[BOLD] FB15k-237 H@10']
Our CapsE performs better than its closely related CNN-based model ConvKB on both experimental datasets (except Hits@10 on WN18RR and MR on FB15k-237), especially on FB15k-237 where our CapsE gains significant improvements of 0.523−0.418=0.105 in MRR (which is about 25.1% relative improvement), and 59.3%−53.2%=6.1% absolute improvement in Hits@10.