## 3 Experimental Results and Analysis
### Triplet Classification  
In the triplet classification tests, we conducted experiments on three datasets: WN11, FB13, and FB15K. On the WN11 dataset, the best configuration for the hyperparameters was found to be \(\alpha=0.001\), \(\gamma=10\), \(k=100\), and \(B=4800\). On the FB13 dataset, the best  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Date Sets** & \multicolumn{3}{c}{**WN18**} & \multicolumn{3}{c}{**FB15K**} \\ \hline \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**MeanRank**} & \multicolumn{2}{c}{**Hits@10/\%**} & \multicolumn{2}{c}{**MeanRank**} & \multicolumn{2}{c}{**Hits@10/\%**} \\ \cline{2-9}  & **Raw** & **Filt** & **Raw** & **Filt** & **Raw** & **Filt** & **Raw** & **Filt** \\ \hline Unstructured [40] & 315 & 304 & 35.3 & 38.2 & 1074 & 979 & 4.5 & 6.3 \\ SE [18] & 1011 & 985 & 68.5 & 80.5 & 273 & 162 & 28.8 & 39.8 \\ SME (Linear) [24] & 545 & 533 & 65.1 & 74.1 & 274 & 154 & 30.7 & 40.8 \\ SME (Bilinear) [24] & 526 & 509 & 54.7 & 61.3 & 284 & 158 & 31.3 & 41.3 \\ LFM [25] & 469 & 456 & 71.4 & 81.6 & 283 & 164 & 26.0 & 33.1 \\ TransE [30] & **263** & **251** & 75.4 & 89.2 & 243 & 125 & 34.9 & 47.1 \\ TransH [31] & 401 & 388 & 73.0 & 82.3 & 212 & 87 & 45.7 & 64.4 \\ TransRFT (unif) & 307 & 290 & 78.3 & **93.0** & 171 & **24.1** & 53.1 & **83.1** \\ TransRFT (bern) & 304 & 288 & **78.4** & 92.8 & **140** & 37.5 & **53.9** & 80.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of link prediction.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Predicting Left (Hits@10)**} & \multicolumn{3}{c}{**Predicting Right (Hits@10)**} \\ \cline{2-9}  & **1-to-1** & **1-to-N** & **N-to-1** & **N-to-N** & **1-to-1** & **1-to-N** & **N-to-1** & **N-to-N** \\ \hline Unstructured [40] & 34.5 & 2.5 & 6.1 & 6.6 & 34.3 & 4.2 & 1.9 & 6.6 \\ SE [18] & 35.6 & 62.6 & 17.2 & 37.5 & 34.9 & 14.6 & 68.3 & 41.3 \\ SME (linear) [24] & 35.1 & 53.7 & 19.0 & 40.3 & 32.7 & 14.9 & 61.6 & 43.3 \\ SME (Bilinear) [24] & 30.9 & 69.6 & 19.9 & 38.6 & 28.2 & 13.1 & 76.0 & 41.8 \\ TransE [30] & 43.7 & 65.7 & 18.2 & 47.2 & 43.7 & 19.7 & 66.7 & 50.0 \\ TransH (unif) [31] & 66.7 & 81.7 & 30.2 & 57.4 & 63.7 & 30.1 & 83.2 & 67.2 \\ TransH (bern) [31] & 66.8 & 87.6 & 28.7 & 64.5 & 65.5 & 39.8 & 83.3 & 67.2 \\ TransRFT (unif) & **92.7** & 96.7 & **65.4** & **76.2** & **92.4** & **71.2** & 95.4 & **78.9** \\ TransRFT (bern) & 92.4 & **97.1** & 56.1 & 75.4 & 91.8 & 58.7 & **96.0** & 78.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hits@10 values of all kinds of relationships in FB15K.  
configuration for the hyperparameters was found to be \(\alpha=0.001\), \(\gamma=5\), \(k=200\), and \(B=4800\). On the FB15K dataset, the best configuration for the hyperparameters was found to be \(\alpha=0.001\), \(\gamma=5\), \(k=100\), and \(B=120\).  
Table 4 shows the results of the triplet classification assessment. It can be seen that in WN11, the TransRFT model is better than TransE model, with an improvement of 12.8%; in FB13, the performance of the TransRFT model is better than the TransE, with an increase of 8.9%. In the FB15K dataset, TransRFT has the best performance. Compared to TransE, the improvement is 12.8%. This shows that TransRFT can adapt and perform well on both sparse and dense datasets.