## 4 Experiment
### Experimental Results and Analysis
#### 4.3.2 Comparison of Machine Learning Methods  
We use classic knowledge graph completion method TransE to embed the medical entities and their relations into low-dimensional continuous entity vectors. Subsequently, we classify treatment relation by employing four machine learning methods to learn the treatment mechanism from the existing treatment relations of drug candidates which is undeveloped in treating [PERSON]'s disease. After that, the trained classifier is used to predict drug candidates to repurpose [PERSON]'s disease. Table 3 shows that the best-performing result has an F1 score of 98.42%, which is trained by SVM classifier in the fused knowledge graph. Additionally, the classification results trained on literature-based knowledge graph are better than the results presented in [PERSON] et al. The reason is that we employ a more rich and complete data sets of the literature. The performance of the machine learning methods that were trained on the fused knowledge graph has a more obvious improvement than other medical knowledge graphs. The most important reason is that we integrate the triples in the medical knowledge base containing a large amount of precise medical information with the literature-based knowledge graph that contains novel medical knowledge. Therefore, we employ machine learning methods in predicting drug candidates against [PERSON]'s disease in order to further confirm the effectiveness of knowledge fusion.  
We employ the t-SNE [29] dimensionality reduction tool to perform dimensionality reduction and visualization processing on the training set in the fused knowledge graph and the literature-based knowledge graph that was used in machine learning methods. The classification effect of Figure 3b has a higher degree of aggregation than the result of Figure 3a, as shown in the Figure 3. The information expression ability of the embedding acquired by entities in fused knowledge graph is significantly better than that of the knowledge graph without fusion. The superiority of knowledge fusion is more intuitively demonstrated through visual display.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**No Knowledge Fusion**} & \multicolumn{3}{c}{**Knowledge Fusion**} \\ \cline{2-7}  & **Hits@1** & **Hits@3** & **Hits@10** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline TransE & 42.11\% & 56.12\% & 69.02\% & **54.94\%** & 62.89\% & 75.59\% \\ DistMult & 28.99\% & 49.74\% & 67.80\% & 36.64\% & 55.08\% & 70.78\% \\ ComplEx & 23.35\% & 45.92\% & 66.58\% & 37.03\% & 51.95\% & 68.44\% \\ ConvE & 28.82\% & 56.68\% & 73.52\% & 36.33\% & **73.52\%** & 75.00\% \\ ConvTransE & 50.95\% & 67.97\% & 86.71\% & 51.72\% & 71.56\% & **87.27\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The drug repurposing results of five knowledge graph completion models.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Literature-Based Knowledge Graph**} & \multicolumn{3}{c}{**Fused Knowledge Graph**} & \multicolumn{3}{c}{**Results in [PERSON] et al. [18]**} \\ \cline{2-9}  & **Recall** & **Precision** & **F1-Score** & **Recall** & **Precision** & **F1-Score** & **Recall** & **Precision** & **F1-Score** \\ \hline SVM & 98.78\% & 96.42\% & 97.58\% & 100.00\% & 96.90\% & **98.42\%** & 98.72\% & 94.14\% & 96.38\% \\ LogisticRegression & 97.55\% & 93.07\% & 95.26\% & 99.92\% & 93.48\% & **96.59\%** & 93.97\% & 91.42\% & 92.68\% \\ RandomForest & 96.56\% & 93.48\% & 95.00\% & 97.12\% & 94.91\% & **96.00\%** & 83.41\% & 93.01\% & 87.95\% \\ DecisionTree & 83.51\% & 81.55\% & 82.52\% & 89.14\% & 82.27\% & **85.57\%** & 72.16\% & 76.13\% & 74.09\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of machine learning methods using different knowledge graph.