Decompressing Knowledge Graph Representations for Link Prediction
Table 4: Performance of different models w/ and w/o decompressing on the testset of FB15k-237 and WN18RR datasets. Results of [♠] and [♡] are taken from dettmers2018convolutional dettmers2018convolutional and sun2019rotate sun2019rotate. -adv sample stands for RotatE without adversarial sampling, which should be a more fair comparison. For each DeCom-enhanced result, the best result is selected from all DeCom settings from Tables 2 and 3.
['Model', '[BOLD] FB15k-237 MRR', '[BOLD] FB15k-237 H@1', '[BOLD] FB15k-237 H@3', '[BOLD] FB15k-237 H@10', '[BOLD] WN18RR MRR', '[BOLD] WN18RR H@1', '[BOLD] WN18RR H@3', '[BOLD] WN18RR H@10']
DeCom vs. others Especially, DeCom-based RESCAL link prediction models achieve state-of-the-art performance on the FB15k-237 dataset across all metrics.