## 4 Experiment
### Experimental Results and Analysis
#### 4.3.1 Comparison of Knowledge Graph Completion Models  
Firstly, we compare the prediction results of the medical candidates using and not using knowledge graph fusion. For the ConvTransE model, the prediction results using knowledge fusion can increase Hits@1 by 0.77%, Hits@3 by 3.59%, and Hits@10 by 0.56% as compared with the result obtained not using knowledge graph fusion, as we can see from Table 2. For the ConvE model, Hits@1 increased by 7.51%, Hits@3 increased by 16.84%, and Hits@10 increased by 1.48%. For the ComplEx model, Hits@1 increased by 13.68%, Hits@3 increased by 6.03%, and Hits@10 increased by 1.86%. For the DistMult model, Hits@1 increased by 7.65%, Hits@3 increased by 5.34%, and Hits@10 increased by 2.98%. For the TransE model, Hits@1 increased by 12.83%, Hits@3 increased by 6.77%, and Hits@10 increased by 6.57%. From the experimental results, we can see that, through knowledge fusion, we not only expand the medical knowledge graph, but also integrate the novel knowledge in the literature-based knowledge graph with the accuracy knowledge in the local medical base. It significantly improves the accuracy of drug repurposing on [PERSON]'s disease.  
Subsequently, we compared the prediction results of drug repurposing among different models. Firstly, we found that the TransE model, as a classic knowledge graph embedding model, is simple, but still achieved high prediction. The TransE model is an classic graph embedding model that treats the relationship as a certain translation vector between entities and it regards the relationship vector \(r\) as the translation between the head entity vector \(h\) and the tail entity vector \(t\) for each triple \((h,r,t)\). However, this model is less effective in dealing with complicated relationships, such as one-to-many and many-to-many. Secondly, the ComplEx model has a good effect on capturing and predicting the semantics of asymmetric relations. The reason is that the ComplEx ([PERSON] et al., 2018) model proposes a method that is based on the representation of complex numbers and it can better capture the semantic information of symmetric relations and make accurate predictions. Subsequently, we can see that the prediction results of the ComplEx model are close to the results of the DistMult model in drug repurposing, because the two models are analogous, and ComplEx introduced complex embedding extends DistMult. The ConvE model has a certain performance improvement when compared with the ComplEx model in the prediction of drug repurposing. The reason is that the ConvE model employs a convolution method. The ConvE model has a good improvement in the prediction results, due to the powerful feature extraction capability of the convolution structure. The ConvTransE model outperforms the ConvE model and it has obtained the best prediction results. The reason is that ConvTransE model adds features of translation on the basis of the ConvE model in order to further improve the accuracy of predictions. Throughout the results, we found that ConvTransE has achieved relatively better results. Therefore, we speculate that ConvTransE method may have better prospects and it is valuable in making further improvements for employing in drug repurposing field.