Decompressing Knowledge Graph Representations for Link Prediction
Table 2: Performance of different models w/ and w/o decompressing on the testset of FB15k-237 dataset. C-DeCom and F-DeCom denotes the CNNs-based and FCNNs-based decompressing functions. *-En means only decompressing entity embeddings and *-Rel shows that only relation embeddings are decompressed. de and ^de denote the dimension of entity features before and after decompressing layer. dr and ^dr represent the dimension of relation features before and after decompressing layer. ’-’ denotes no decompressing layer in the model. ‘vanilla expansion‘ means explicitly increasing the embedding dimension (same notation are followed in other tables).
['[BOLD] #', 'Model', '[ITALIC] de', '^ [ITALIC] de', '[ITALIC] dr', '^ [ITALIC] dr', '[BOLD] FB15k-237 MRR', '[BOLD] FB15k-237 H@1', '[BOLD] FB15k-237 H@3', '[BOLD] FB15k-237 H@10']
w/ and w/o decompressing on the testset of FB15k-237 and WN18RR datasets. Results of [♠] and [♡] are taken from \citeauthordettmers2018convolutional \shortcitedettmers2018convolutional and \citeauthorsun2019rotate \shortcitesun2019rotate. -adv sample stands for RotatE without adversarial sampling, which should be a more fair comparison. C-DeCom vs. F-DeCom: One reason that F-DeCom achieving higher scores is that it could extract features from all embedding dimensions but C-DeCom is only able to extract features in the range of kernels. DeCom-En vs. We attribute this to that modelling relation between entities is more complicated which needs more expressive and robust features from DeCom. DeCom vs. others Especially, DeCom-based RESCAL link prediction models achieve state-of-the-art performance on the FB15k-237 dataset across all metrics.