## 3 Experimental Results and Analysis
### Link Prediction  
In this paper, unif represents the traditional method of replacing head and tail entities with equal probability, and bern represents the method of adopting Bernoulli sampling. During the model training process, we adjust hyperparameters to achieve the best performance of the model. Among them, the learning rate \(\alpha\) is chosen from the set {0.01, 0.001, 0.0001, 0.00001}. The boundary \(\gamma\) can take values from the set {0.5, 1, 2, 3, 4, 5, 6}. The dimension \(k\) can take values from the set {50, 100, 150, 200, 250, 300}. The batch size \(B\) can take values from the set {600, 1200, 4800, 6000}. We evaluate the performance of the TransRFT using two metrics: MeanRank and Hit@10%. MeanRank measures the average rank of the true head entity among the predicted entities for each test triple. A lower rank indicates more accurate predictions. Hit@10% measures whether the true entity is included in the top 10% of predicted entities. A higher Hit@10% indicates better prediction capability. Typically, we use the term \"Raw\" to refer to link prediction without any dataset processing, and \"Filter\" to refer to link prediction with the removal of positive triples. Using the Raw metric allows us to assess the overall prediction capability of the model, while using the Filter metric provides a more accurate evaluation of the model's ability to predict unknown entity relationships. In our case, we perform iterative training for 500 epochs on all triples in the WN18 and FB15K datasets. When the sampling method is \"unif\", the TransRFT model performs best on the WN18 dataset with parameter settings of \(\alpha=0.0001\), \(\gamma=5\), \(k=100\)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Entities** & **Relationships** & **Train** & **Verify** & **Test** \\ \hline WN18 & 40,943 & 18 & 141,442 & 5000 & 5000 \\ WN11 & 38,696 & 11 & 112,581 & 2609 & 10,544 \\ FB15K & 14,951 & 1345 & 483,142 & 50,000 & 59,071 \\ FB13 & 75,043 & 13 & 316,232 & 5908 & 23,733 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Datasets.  
and \(B=4800\). On the FB15K dataset, the TransRFT model performs best with parameter settings of \(\alpha=0.001\), \(\gamma=4\), \(k=200\), and \(B=4800\). When the sampling method is \"bern,\" the TransRFT model performs best on the WN18 dataset with parameter settings of \(\alpha=0.0001\), \(\gamma=5\), \(k=100\), and \(B=4800\). On the FB15K dataset, the TransRFT model performs best with parameter settings of \(\alpha=0.001\), \(\gamma=4\), \(k=200\), and \(B=4800\). The experimental results of the link prediction are shown in Table 2.  
Through our experiments, we found that on the WN18 dataset, TransRFT (unif) and TransRFT (bern) outperform MeanRank in terms of the Hit@10% metric. This is because the WN18 dataset contains fewer entities and relations, and TransRFT is able to make predictions about entities, although its accuracy is not as high as TransE. On the FB15K dataset, TransRFT (unif) and TransRFT (bern) perform better overall compared to other methods. In terms of the Hits@10 metric, TransRFT improves by 3.8% on WN18 and by 35.9% on FB15K compared to TransE, showing a significant improvement in performance.  
In order to confirm that TransRFT can better handle complex relationships, we chose the FB15K dataset with more relationships and utilized the optimal configuration parameters for Hits@10 to test the scores under 1-to-1, 1-to-n, n-to-1, and n-to-n relationships, respectively, and ranked the scores. From the experimental results in Table 3, it can be seen that the TransRFT model achieves the best results for both predicting left and predicting right, outperforming the other models. Among them, TransRFT achieves 97.1% on predicting left with a 1-to-n relationship and 96.0% on predicting right with a n-to-1 relationship.