# Link Prediction Based on Feature Mapping and Bi-Directional Convolution
## 5 Ablation Experiments  
Given the strong experimental results of the proposed method, a closer examination of the specific contributions of its components is warranted. Knowledge embedding of multi-hop graph attention and one-way convolution are used as the basis of the model, and two-way convolutional networks, feature mapping, and R-drop were used as variables to set up controlled experiments. First, in the experiments, all the triples were encoded through the coding layer to acquire the coded representation of entities and relationships. Next, experiments were conducted on two datasets by adding bi-directional convolution and feature mapping, and evaluation metrics such as Hits@1, Hits@3, Hits@10, and MRR were observed. The results showed that the model tends to stabilize after about 200 rounds of training. Thereafter, a control experiment was conducted, and the MR evaluation metrics of the three models on two datasets were observed. The experimental results show that after about 200 rounds of training, the models basically converge and achieve the optimal values among the five metrics. This experiment used the coding result of the last epoch of the coding layer as the coded representation of entities and relations. After the feature mapping layer, the entities were mapped into the relationship space. In the decoding layer, the data of each evaluation index were counted every ten rounds. The results in Figure 2 show that after 200 rounds of training, the three models basically converge. Figures 2-4 show the specific results of the three models on the two datasets except for the MR evaluation metrics. Figure 5 shows the specific results of the three models on the two datasets for the MR evaluation metrics. The results show that the five metrics achieved optimal values at close to about 200 epochs. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & \multicolumn{5}{c}{**WN18 RR**} & \multicolumn{5}{c}{**FB15K-237**} \\ \hline  & MR & MRR & @1 & @3 & @10 & MR & MRR & @1 & @3 & @10 \\ TransE & 2300 & 0.243 & 4.27 & 44.1 & 53.2 & 323 & 0.279 & 19.8 & 37.6 & 44.1 \\ ConvE & 4464 & 0.456 & **41.9** & 47 & 53.1 & 245 & 0.312 & 22.5 & 34.1 & 49.7 \\ ConvKB & **1295** & 0.265 & 5.82 & 44.5 & 55.8 & 216 & 0.289 & 19.8 & 32.4 & 47.1 \\ DisMuit & 7000 & 0.444 & 41.2 & 47 & 50.4 & 512 & 0.281 & 19.9 & 30.1 & 44.6 \\ ComplEX & 7882 & 0.449 & 40.9 & 46.9 & 53 & 546 & 0.278 & 19.4 & 29.7 & 45 \\ KB-GAT & 1940 & 0.440 & 36.1 & 48.3 & 58.1 & 210 & **0.518** & **46** & 54 & 62.6 \\ ConvFM & 1421 & **0.475** & 40.2 & **51.1** & **60.7** & **126** & 0.515 & 45.2 & **55.6** & **66.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different models for link prediction, with the best results highlighted in bold. Figure 4: Results of ConGAT-BI-FM-rDrop. Figure 3: Results of ConGAT-BI-FM. Figure 2: Results of ConGAT-BI. When facing sparse and non-overlapping relations, our model was also able to show excellent performance. Multi-hop graph attention neural networks can provided significant advantages when dealing with these relations via the model's mapping mechanism. Graph neural networks are particularly good at aggregating sparse relational links in the knowledge graph through a sparse representation of the adjacency matrix. In addition, through the mapping layer of the model, we can effectively identify and process non-overlapping relations and realize accurate mapping. From now on, we will also focus on conducted detailed experiments on sparse or non-overlapping relation processing to further validate our conclusions and continuously improve the performance of the model in these areas. The experiments used the coding layer of the graph attention network and the one-way convolution as the basis of the overall model (ConGAT). In the case of the basic convergence of the model, the models of all three control experiments performed well, as shown by Table 3, where the bi-directional convolution, feature mapping, and rDrop all led to different degrees of model improvement.