# Adaptive Attentional Network for Few-Shot Relational Learning of Knowledge Graphs
## 5 Experiments
### Ablation Analysis  
The main improvement of the method in this paper is the entity augmentation encoder, which mainly includes Transformer and hierarchical attention mechanism. The Transformer mechanism has been widely proven to be effective for weights. In order to further verify the effectiveness of the hierarchical attention mechanism, further ablation analysis is performed in this paper. The experimental results are shown in Table 3. In Table 3, Two level represents the model using two-layer aggregation layers in related fields, Three level represents the model using three-layer aggregation layers in related fields, and the underlined numbers  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**NELL-One**} & \multicolumn{2}{c}{**MRR**} & \multicolumn{2}{c}{**Hits@1**} & \multicolumn{2}{c}{**Hits@5**} & \multicolumn{2}{c}{**Hits@10**} \\ \cline{2-9}  & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline TransE & 9.3 & 17.4 & 4.3 & 10.1 & 14.1 & 23.1 & 19.2 & 31.3 \\ DistMult & 10.2 & 20.2 & 6.6 & 13.7 & 12.6 & 25.1 & 17.7 & 31.3 \\ ComplEx & 13.1 & 18.4 & 8.6 & 11.8 & 8.6 & 22.9 & 22.3 & 29.7 \\ RotatE & - & 17.6 & - & 10.1 & - & 24.7 & - & 32.9 \\ \hline GMatching\_RESCAL & 18.8 & - & 13.3 & - & 24.3 & - & 30.5 & - \\ GMatching\_TransE & 17.1 & - & 12.2 & - & 21.0 & - & 25.5 & - \\ GMatching\_DistMult & 17.1 & - & 11.4 & - & 22.1 & - & 30.1 & - \\ GMatching\_CompEx & 18.5 & 20.1 & 11.9 & 14.3 & 26.0 & 26.4 & 31.3 & 31.1 \\ GMatching\_Random & 15.1 & - & 10.3 & - & 18.6 & - & 25.2 & - \\ \hline Meta(BG:Pre-Train) & 16.4 & 20.9 & 9.3 & 14.1 & 23.8 & 18.0 & 33.1 & 33.5 \\ \hline FSRL & - & 15.3 & - & 7.3 & - & 21.2 & - & 35.5 \\ \hline HAF & 22.6 & **22.5** & 17.1 & **15.9** & 28.3 & **29.0** & 34.7 & 35.7 \\ Ours & **26.0** & 22.4 & **18.1** & 14.9 & **33.0** & **29.0** & **41.0** & **39.2** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The 1-shot and 5-shot experimental results on the NELL-One dataset. Underline represents the best experimental result of GMatching, and the numbers in bold represent the best experimental results of all methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**NELL-One**} & \multicolumn{2}{c}{**MRR**} & \multicolumn{2}{c}{**Hits@1**} & \multicolumn{2}{c}{**Hits@5**} & \multicolumn{2}{c}{**Hits@10**} \\ \cline{2-9}  & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** & **1-Shot** & **5-Shot** \\ \hline GMatching(mean) & 17.6 & - & 11.1 & - & 24.8 & - & 28.8 & - \\ \hline HAF(mean) & 19.5 & 19.9 & 13.0 & **13.3** & 25.6 & **25.9** & 32.2 & 32.7 \\ Ours(mean) & **22.5** & **20.0** & **14.5** & 12.0 & **29.2** & 25.8 & **38.1** & **35.9** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average results of 1-shot and 5-shot experiments of our method and the baseline method on the NELL-One dataset. Bold indicates the best experimental result.  
represent the best results with the highest performance in the two-layer and three-layer models. Bold numbers represent the best results for the average performance of the two models.  
The highest and average performance of the model with three aggregation layers in Table 3 is higher than that of the model with two aggregation layers. The experimental results show that when the number of aggregation layers is larger within the threshold, the experimental results in this paper are better. The threshold value in this paper is 3 because the computational complexity increases exponentially with the larger the number of aggregation layers, and the noise error becomes larger. In summary, the effectiveness of the hierarchical attention mechanism of our method can be demonstrated.