An Interpretable Knowledge Transfer Model for Knowledge Base Completion
Table 4: Different methods to obtain sparse representations
['[BOLD] Method', '[BOLD] WN18 MR', '[BOLD] WN18 H10', '[BOLD] FB15k MR', '[BOLD] FB15k H10']
On both benchmarks, ITransF achieves significant improvement against sparse encoding on pretrained model. This performance gap should be expected since the objective function of sparse encoding methods is to minimize the reconstruction loss rather than optimize the criterion for link prediction.