# Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding
## 4 Experiments
### Comparison with Other Negative Sampling Methods  
Due to the lack of special requirements for the scoring function in the model discriminator, we used scoring functions from multiple models as discriminators. Four negative sampling methods, namely, uniform random negative sampling, KBGAN, NSCaching, and EANS [40], were used to make comparisons with the negative sampling method proposed in this paper to verify the effectiveness of this method. The experimental results are shown in Table 4.  
In Table 4, Uniform, KBGAN, NSCaching, and EANS [40] represent the current state-of-the-art negative sampling methods. This study utilized these methods along with the GNS method proposed herein to examine the impact of different negative sampling techniques on the model's link prediction performance across distance models and tensor decomposition models. The results of the other methods in the table were sourced from published articles on the respective models, with bold fonts indicating the highest outcome. Upon scrutiny of the results provided in the table, it becomes evident that GNS consistently outperforms alternative negative sampling methods across the majority of models. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **FB15k-237** & \multicolumn{6}{c}{**WN18 RR**} \\ \hline  & **MRR** & **H@1** & **H@3** & **H@10** & **MRR** & **H@1** & **H@3** & **H@10** \\ \hline TransE & 0.33 & 0.231 & 0.369 & 0.528 & 0.223 & 0.014 & 0.401 & 0.529 \\ RotatE & 0.338 & 0.241 & 0.375 & 0.533 & 0.476 & 0.428 & 0.492 & **0.571** \\ PairRE & 0.351 & 0.256 & 0.387 & 0.544 & - & - & - & - \\ DistMult & 0.308 & 0.219 & 0.336 & 0.485 & 0.439 & 0.394 & 0.452 & 0.533 \\ ComplEx & 0.323 & 0.229 & 0.353 & 0.513 & 0.468 & 0.427 & 0.485 & 0.554 \\ TuckER & 0.358 & 0.266 & 0.394 & 0.544 & 0.47 & 0.43 & 0.482 & 0.526 \\ ConvE & 0.325 & 0.237 & 0.356 & 0.501 & 0.43 & 0.4 & 0.44 & 0.52 \\ InteractE & 0.354 & 0.263 & - & 0.535 & 0.463 & 0.43 & - & 0.528 \\ PROCRUSTES & 0.345 & 0.249 & 0.379 & 0.541 & 0.474 & 0.421 & 0.502 & 0.569 \\
**KGE-EML**[11] & 0.36 & **0.267** & 0.395 & 0.545 & 0.477 & 0.444 & 0.492 & 0.534 \\
**KGE-EML+GNS** & **0.365** & 0.265 & **0.398** & **0.55** & **0.487** & **0.447** & **0.506** & 0.569 \\ \hline \hline \end{tabular} The bold text here in the “Model” column represents our proposed model, while under each evaluation metric, it signifies the best results.  
\end{table} 
Table 2: Link prediction results on FB15k-237 and WN18 RR. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **1-to-1** & **1-to-n** & **n-to-1** & **n-to-n** \\ \hline ComplEx & **0.939** & 0.896 & 0.822 & 0.902 \\ TransE & 0.887 & 0.822 & 0.766 & 0.895 \\ RotatE & 0.923 & 0.84 & 0.782 & 0.908 \\ PaiRE & 0.785 & 0.899 & 0.872 & 0.94 \\
**Ours** & 0.865 & **0.948** & **0.875** & **0.913** \\ \hline \hline \end{tabular} The bold text here in the “Model” column represents our proposed model, while under each complex relation, it signifies the best results.  
\end{table} 
Table 3: Capacity for handling complex relations: we conducted the experiment on FB15k and show the Hit@10 results in the table. In order to verify whether the gradient vanishing problem is mitigated, this study used the uniform sampling method UNIFORM and the GNS method proposed in this paper to train the model, respectively; then, we plotted the change curves of the training loss and the accuracy index Hit@10 as the training proceeded. The experiments were conducted on TransE and the proposed model using two datasets, FB15k-237 and WN18 RR, and the results are shown in Figure 6. First, it can be seen that although the losses of GNS and Uniform converge gradually, they do not reduce to zero because the small sampling process introduces noise into the gradient.