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Hi. The original paper only uses 50 for the latent dimension size (we use 400). You can see this paper https://arxiv.org/pdf/1903.12287.pdf (Table-2) to compare our results with pytorch-biggraph (using 400 dim_size) Actually if we set dim_size to 400, the HITS@10 result of the original paper implementation is 0.74. You can also see some other open-source tools' results: https://github.com/thunlp/KB2E In KB2E, using dim_size 100 the HITS@10 is 0.702.
Hi, I found that your hit@10 result on transE is 0.8x, but in fact the original paper is 0.4x, I am puzzled about this.
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