# A Knowledge Graph Embedding Model Based on Cyclic Consistency--Cyclic CKGE
## 5 Experiment
### Intelligent Medical System Function Test  
The intelligent medical diagnosis system constructed in this paper has three query methods, which are conditional query results, path query results and multi-symptom disease inference results. In the intelligent medical diagnosis system, relevant disease information can be searched through conditional search. Enter the name of the disease, location of the disease, infectivity, population of the disease and other relevant information  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**(a)**} \\ \hline  & \multicolumn{3}{c}{**WN18**} \\  & **MR** & **MRR** & **hit@3** & **hit@10** \\ \hline TransE & 401 & — & — & 0.892 \\ DistMult & 902 & 0.822 & 0.914 & 0.936 \\ Cyclic\_CKGE & 251 & 0.942 & 0.968 & 0.969 \\ \hline  & \multicolumn{3}{c}{**(b)**} \\  & \multicolumn{3}{c}{**FB15k**} \\  & \multicolumn{3}{c}{**MR**} & **MRR** & **hit@3** & **hit@10** \\ \hline TransE & 125 & — & — & 0.471 \\ DistMult & 97 & 0.654 & 0.733 & 0.824 \\ Cyclic\_CKGE & 64 & 0.745 & 0.801 & 0.873 \\ \hline \hline \end{tabular}
\end{table}
Table 6: (a). WN18 link prediction results. (b). FB15k link prediction results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**(a)**} \\ \hline  & \multicolumn{3}{c}{**WN18 RR**} \\  & **MR** & **MRR** & **hit@3** & **hit@10** \\ \hline TransE & 4166 & 0.231 & 0.352 & 0.499 \\ DistMult & 5110 & 0.430 & 0.440 & 0.490 \\ Cyclic\_CKGE & 3288 & 0.460 & 0.450 & 0.540 \\ \hline  & \multicolumn{3}{c}{**(b)**} \\  & \multicolumn{3}{c}{**MR**} & **MRR** & **hit@3** & **hit@10** \\ \hline TransE & 355 & 0.295 & 0.291 & 0.464 \\ DistMult & 254 & 0.241 & 0.263 & 0.419 \\ Cyclic\_CKGE & 246 & 0.316 & 0.350 & 0.491 \\ \hline \hline \end{tabular}
\end{table}
Table 7: (a). Link prediction results for WN18 RR. (b). Link prediction results for FB15k-237.  
in the search box, as shown in Figure 8, and the related diseases can be searched. For example, by searching the information of the disease name \"Streptococcus pneumoniae septicemia\", the relevant disease information can be obtained as shown in Figure 9.  
In the path search of the intelligent medical diagnosis system, the relationship between the two nodes can be inferred by selecting the start node and the end node, as shown in Figure 10. For example, in the start node, select diabetes, and in the end node, select diabetes, then the relationship between the two nodes with complications can be retrieved to form a closed loop, as shown in Figure 11.  
In the multi-symptom disease inference, the patient's input of \"eye pain, fear of light, accompanied by tears\" is taken as an example, as shown in Figure 12. At this time, the system can predict the corresponding diseases and deduce complications from the visual interface according to the knowledge graph constructed previously, as shown in Figure 13.  
Figure 8: Search mode of conditional query.  
Figure 9: Disease information search results.  
Figure 11: Path search inference results.  
Figure 12: Multi-symptom disease inference.  
Figure 10: Select disease nodes for inference.