Decompressing Knowledge Graph Representations for Link Prediction
Table 5: Comparison between models with different types of DeCom layers on the validation set of FB15k-237. The speed is calculated by the number of triples processed per second during predicting (validation) time. DeCom size means the size of features after decompressing layer.
['Model', 'Embed. Size', 'DeCom Size', 'MRR', 'H@10', 'Param. Size', 'Speed (triples/sec)']
The decompressing layer is able to map the original embedding to a more expressive and robust feature space. One natural question is: what if we explicitly increase the embedding size? Therefore, we increase the embedding size from 100 to 400 to match the feature size after decompressing layer and compare them from different perspectives. It is clear to find that models with decompressing layer not only achieve better performance, but are much more parameter efficient with a little sacrifice of prediction speed. Especially, ComplEx with large embedding size instead harms the performance. We attribute this to the overfitting of two many parameters. Comparing with C-DeCom, F-DeCom obtains better scores with a little more parameters and slower decoding speed.