## 4 Results
### Experiment Results  
**KGC tasks on benchmarks. ** As shown in Table 1, in the KGC tasks, the performance of Relgraph is optimal on most metrics. Relgraph-TransE and Relgraph-RotatE have better performance than other representation-based methods such as TransE, RotatE, and ConvE. For MRR on FB15K-237 and UMLS database, Relgraph-RotatE is 56.5% and 22.0% higher than the best algorithm before Relgraph and the improvement of Relgraph-TransE is 52.9% and 21.6%, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**WN1 SRR**} & \multicolumn{3}{c}{**FB15K-237**} & \multicolumn{3}{c}{**UMLS**} \\  & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline TransE * & 0.222 & 1.3 & 40 & 53 & 0.3 & 23 & 36.9 & 52.7 & - & - & - & - \\ DistMult & 0.444 & 41.2 & 47 & 50.4 & 0.281 & 19.9 & 30.1 & 44.6 & 0.391 & 25.6 & 44.5 & 66.9 \\ ConvE * & 0.456 & 41.9 & 47 & 53.1 & 0.312 & 22.5 & 34.1 & 49.7 & - & - & - & - \\ CompGCN & 0.473 & 42.6 & 47.8 & 55.1 & 0.337 & 23.8 & 35.1 & 49.9 & 0.721 & 58.6 & 81.1 & 90.1 \\ RotatE & 0.476 & 42.8 & 49.2 & 57.1 & 0.338 & 24.1 & 37.5 & 53.3 & 0.744 & 63.6 & 82.2 & 93.9 \\ Ours:Relgraph-TransE & 0.478 & 41.3 & 51.9 & 59.9 & 0.517 & 45.7 & 54.4 & 63.6 & 0.905 & 84.4 & 95.8 & 98.3 \\ Ours:Relgraph-RotatE & **0.485** & **43.2** & **52.5** & **61.5** & **0.529** & **46.7** & **55.6** & **64.9** & **0.908** & **86.6** & **94.1** & **97.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of knowledge graph completion (KGC) tasks on benchmarks. * denotes results from publications. - denotes the unpublished results. The experimental results indicate that the KGC performance of Relgraph-RotatE and Relgraph-TransE exceeds traditional methods. **The impact of relation graph on enhancing the performance of reasoning. ** Table 2 shows the improvement brought by relation graph in embedding learning and KG reasoning. It can be found that the model version using relation graph (Relgraph-RotatE or Relgraph-TransE) has certain advantages in performance compared to the version not used (GAT-RotatE or GAT-TransE). We use a bar chart to visually demonstrate the improvement of KG reasoning ability through relation graph in Relgraph, as shown in Figure 3.  
**Drug repurposing. ** As shown in Table 3, in the drug repurposing experiment, Relgraph performed best in most metrics. Relgraph-TransE and Relgraph-RotatE had better performances than baseline methods, such as TransE, RotatE, and ConvE. Relgraph-RotatE was 4.4% higher than the best algorithm before Relgraph, and Relgraph-TransE was 1.3% higher than the original TransE. The model version using relation graph (Relgraph-RotatE or Relgraph-TransE) also has advantages in performance compared to the version not used (GAT-RotatE or GAT-TransE). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**WN18 RR**} & \multicolumn{3}{c}{**FB15K-237**} & \multicolumn{3}{c}{**UMLS**} \\  & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline TransE * & 0.222 & 1.3 & 40 & 53 & 0.3 & 23 & 36.9 & 52.7 & - & - & - & - \\ RotatE & 0.476 & 42.8 & 49.2 & 57.1 & 0.338 & 24.1 & 37.5 & 53.3 & 0.744 & 63.6 & 82.2 & 93.9 \\ GAT-RotatE & 0.472 & 39.1 & 51.8 & 60.7 & 0.501 & 44.5 & 52.6 & 62.7 & 0.862 & 76.5 & 91.3 & 96.7 \\ GAT-TransE & 0.468 & 38.5 & 50.0 & 56.9 & 0.488 & 43.2 & 51.1 & 60.7 & 0.829 & 73.7 & 88.3 & 95.0 \\ Relgraph-RotatE & **0.485** & **43.2** & **52.5** & **61.5** & **0.529** & **46.7** & **55.6** & **64.9** & **0.908** & **86.6** & **94.1** & **97.2** \\ Relgraph-TransE & 0.478 & 41.3 & 51.9 & 59.9 & 0.517 & 45.7 & 54.4 & 63.6 & 0.905 & 84.4 & 95.8 & 98.3 \\ \hline \hline \end{tabular}
\end{table}