An Interpretable Knowledge Transfer Model for Knowledge Base Completion
Table 5: Different λ’s effect on our model performance. The compared models are trained for 2000 epochs
['[BOLD] Method', '[BOLD] WN18 MR', '[BOLD] WN18 H10', '[BOLD] FB15k MR', '[BOLD] FB15k H10']
We compare how different value of λ would influence our model’s performance in Table. With large λ and higher domain sampling probability, our model’s Hits@10 increases while mean rank also increases. The rise of mean rank is due to higher probability of generating a valid triple as a negative sample causing the energy of a valid triple to increase, which leads to a higher overall rank of a correct entity. However, the reasoning capability is boosted with higher Hits@10 as shown in the table.