kbgan: Adversarial Learning for Knowledge Graph Embeddings
Table 4: Experimental results. Results of kbgan are results of its discriminator (on the left of the “+” sign). Underlined results are the best ones among our implementations. Results marked with † are produced by running Fast-TransX Lin et al. (2015) with its default parameters. Results marked with ‡ are copied from Dettmers et al. (2017). All other baseline results are copied from their original papers.
['Method', '[BOLD] FB15k-237 MRR', '[BOLD] FB15k-237 H@10', '[BOLD] WN18 MRR', '[BOLD] WN18 H@10', '[BOLD] WN18RR MRR', '[BOLD] WN18RR H@10']
All settings of adversarial training bring a pronounced improvement to the model, which indicates that our method is consistently effective in various cases. TransE performs slightly worse than TransD on FB15k-237 and WN18, but better on WN18RR. Using DistMult or ComplEx as the generator does not affect performance greatly.