Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion
Table 5: Parameter trade-off analysis on FB15K-237.
['Emb. size', 'Param. count', '\\mathsfit  [ITALIC] W size', 'MRR', 'H@ 1', 'H@ 3', 'H@ 10']
There are two kinds of parameters in the MEI model, the embeddings and the core tensors. For example, on FB15K-237, the parameter efficiency increases when the partition size increases up to C≈122. However, there are other factors affecting this trade-off, such as the ensemble boosting effect that favors larger K and smaller C. We argue that due to this effect, MEI with K>1 has an empirical advantage compared with MEI with K=1. To evaluate this claim, we analyze the performance of MEI models with approximately the same parameter counts but different core-tensor sizes on FB15K-237. To disambiguate the effects of larger core tensor, we made sure that the models with larger core tensors would have smaller parameter counts. Interestingly, MEI with K=3 achieves competitive results compared with MEI with K=1, which suggest that the ensemble boosting effect benefits MEI with K>1, as we argued.