# Convolutional Adaptive Network for Link Prediction in Knowledge Bases
## 5 Results and Discussion  
### Experimental Results  
Tables 2-4 present the results of the link prediction tasks. The second-best score is underlined and the best score is in bold. We observed the following:  
* The proposed methods surpassed the other baseline methods in most instances. For the majority of the metrics, CANet achieved state-of-the-art results. As a result, the proposed methods could produce impressive link prediction results by using the CNN's parameter-efficient and fast operators. In comparison to the optimal methods on the WN18 RR, FB15k-237, and YAGO3-10 datasets, CANet improved the MRR by 4.3%, 5.8%, and 14.5%, respectively. This proved the efficacy and applicability of our method.
* Semantic matching methods are simple to learn and could be used to represent a knowledge base through the training process. However, because of their redundancy, they were highly susceptible to overfitting, resulting in lower performance than that of the proposed approaches.
* Because the former created the weights of convolutional kernels that were relevant to each reference, CANet surpassed ConvE on all metrics. As a result, multiple relations may be used to obtain various feature maps. As a result, CANet is better at identifying the differences between entities.  
To summarize, the proposed link prediction method is highly expressive. The proposed method had considerable improvements for all evaluation metrics in KBs because of its structure. It could also improve the efficiency by generating particular convolutional kernel weights to study different properties with the entity embeddings for each relation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**WN18**} & \multicolumn{4}{c}{**FB15k**} \\ \cline{2-9}
**Model** & \multicolumn{4}{c}{**Hits**} & \multicolumn{4}{c}{**Hits**} & \multicolumn{4}{c}{**Hits**} \\ \cline{3-9}  & **MRR** & \multicolumn{2}{c}{**@10**} & \multicolumn{1}{c}{**@3**} & \multicolumn{1}{c}{**@1**} & \multicolumn{1}{c}{**MRR**} & \multicolumn{2}{c}{**@10**} & \multicolumn{1}{c}{**@3**} & \multicolumn{1}{c}{**@1**} \\ \hline TransE & 0.454 & 0.934 & 0.823 & 0.089 & 0.380 & 0.641 & 0.472 & 0.231 \\ DistMult & 0.822 & 0.936 & 0.914 & 0.728 & 0.654 & 0.824 & 0.733 & 0.546 \\ ComplEx & 0.941 & 0.947 & 0.936 & 0.936 & 0.692 & 0.840 & 0.759 & 0.599 \\ R-GCN & 0.814 & 0.955 & 0.928 & 0.686 & 0.696 & 0.842 & 0.760 & 0.601 \\ ConvE & 0.943 & **0.956** & 0.946 & 0.935 & 0.657 & 0.831 & 0.723 & 0.558 \\ TorusE & 0.947 & 0.954 & 0.950 & 0.943 & 0.733 & 0.832 & 0.771 & 0.674 \\ \hline CANet & **0.950** & 0.955 & **0.952** & **0.945** & **0.753** & **0.862** & **0.796** & **0.684** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The MRR and Hits@k of the link prediction results on the WN18 and FB15k datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**WN18 RR**} & \multicolumn{4}{c}{**FB15k-237**} \\ \cline{2-9}
**Model** & \multicolumn{4}{c}{**Hits**} & \multicolumn{4}{c}{**Hits**} & \multicolumn{4}{c}{**Hits**} \\ \cline{3-9}  & **MRR** & \multicolumn{2}{c}{**@10**} & \multicolumn{1}{c}{**@3**} & \multicolumn{1}{c}{**@1**} & \multicolumn{1}{c}{**MRR**} & \multicolumn{2}{c}{**@10**} & \multicolumn{1}{c}{**@3**} & \multicolumn{1}{c}{**@1**} \\ \hline TransE & 0.182 & 0.444 & 0.295 & 0.027 & 0.257 & 0.420 & 0.284 & 0.174 \\ DistMult & 0.430 & 0.490 & 0.440 & 0.390 & 0.241 & 0.419 & 0.263 & 0.155 \\ ComplEx & 0.440 & 0.510 & 0.460 & 0.410 & 0.247 & 0.428 & 0.275 & 0.158 \\ R-GCN & - & - & - & - & 0.249 & 0.417 & 0.264 & 0.151 \\ ConvE & 0.430 & 0.520 & 0.440 & 0.400 & 0.325 & 0.501 & 0.356 & 0.237 \\ TorusE & 0.452 & 0.512 & 0.464 & 0.422 & 0.316 & 0.484 & 0.335 & 0.217 \\ \hline CANet & **0.478** & **0.532** & **0.489** & **0.445** & **0.339** & **0.530** & **0.378** & **0.251** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The MRR and Hits@k of the link prediction results on the WN18 RR and FB15k-237 datasets.