## 4 Experiments
### Experimental Design
#### 4.4.2 Analysis of Experimental Results of Op-Trans  
In this section, we will explore the effect of the weights of triplet-mapping properties on the underlying model mentioned above. We still adopted the comparison method introduced in Section 4.4.1, taking the CCS negative sampling optimization method as a prerequisite for the KGE models, so that the analysis results on the base model can be more accurate and the subsequent opportunity to further enhance the base algorithm is available. We integrated the triplet-mapping-property weights with the CCS negative sampling optimization method to form the knowledge-graph optimization framework Op-Trans. We tested the predictive power of Op-Trans on four data sets. Table 6 shows the experimental results of these translation models after using the Op-Trans learning framework. On the WN18 RR, Op-Trans improves on Hits@10 by approximately 13% compared to TransE, and 6% on Hits@10 compared to CCS + TransE (which takes our proposed negative sampling method for TransE only). On the FB15K237, Op-TransE improves on Hits@10 by approximately 16% compared to TransE. Meanwhile, our Op-Trans learning framework achieved a good improvement on the results of other baseline models of KGE. The Op-Trans learning framework enables a better performance of the family of translation models. Figures 9 and 10 show the comparison results of Hits@10 for baseline models. Baseline models using only the CCS negative sampling optimization method, and baseline models using Op-Trans framework are compared with the baseline model on two datasets: WN18 RR and FB15K237, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**WN18 RR**} & \multicolumn{3}{c}{**FB15K237**} \\ \cline{2-7}  & **MR** & **MRR** & **Hits@10** & **MR** & **MRR** & **Hits@10** \\ \hline Op-TransE & 4171 & 0.2122 & 0.5102 & 177 & 0.2415 & 0.4852 \\ Op-TransH & 4404 & 0.2307 & 0.5121 & 180 & 0.2893 & 0.4815 \\ Op-TransR & 3685 & 0.2415 & 0.5217 & 169 & 0.2993 & 0.4873 \\ Op-TransD & 3352 & 0.2900 & 0.5425 & 182 & 0.3010 & 0.4924 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the performance of the Op-Trans framework on the baseline model on two datasets, WN18 RR and FB15K237. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**WN18 RR**} & \multicolumn{3}{c}{**FB15K237**} \\ \cline{2-7}  & **Metrics** & **MR** & **MRR** & **Hits@10** & **MR** & **MRR** & **Hits@10** \\ \hline \multirow{4}{*}{TransE} & Bernoulli & **3924** & 0.1784 & 0.4509 & 197 & 0.2556 & 0.4189 \\  & Kbgan & 5356 & 0.1808 & 0.4324 & 722 & 0.2926 & 0.4659 \\  & NSCaching & 4472 & 0.2002 & 0.4783 & 186 & 0.2993 & 0.4764 \\  & CCS & 4481 & **0.2031** & **0.4809** & **182** & **0.3101** & **0.4802** \\ \hline \multirow{4}{*}{TransH} & Bernoulli & 4113 & 0.1862 & 0.4509 & 202 & 0.2329 & 0.4010 \\  & Kbgan & 4881 & 0.1869 & 0.4481 & 455 & 0.2779 & 0.4619 \\  & NSCaching & 4491 & 0.2041 & 0.4804 & **185** & 0.2832 & 0.4659 \\  & CCS & **4337** & **0.2273** & **0.4894** & 187 & **0.2897** & **0.4726** \\ \hline \multirow{4}{*}{TransR} & Bernoulli & 3824 & 0.1884 & 0.4603 & 191 & 0.2397 & 0.4201 \\  & Kbgan & 4572 & 0.1871 & 0.4521 & 793 & 0.2424 & 0.4431 \\  & NSCaching & **3639** & 0.2010 & 0.4822 & 181 & 0.2751 & 0.4773 \\  & CCS & 3801 & **0.2300** & **0.4891** & **174** & **0.2822** & **0.4830** \\ \hline \multirow{4}{*}{TransD} & Bernoulli & 3555 & 0.1901 & 0.4641 & 188 & 0.2451 & 0.4289 \\  & Kbgan & 4083 & 0.1875 & 0.4641 & 825 & 0.2465 & 0.4440 \\ \cline{1-1}  & NSCaching & **3104** & 0.2013 & 0.4839 & 189 & 0.2863 & 0.4785 \\ \cline{1-1}  & CCS & 3317 & **0.2415** & **0.5297** & **177** & **0.2890** & **0.4852** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of various negative sampling methods on two datasets, WN18 RR and FB15K237. Bold indicates the best performance. Table 7 summarizes our results on WN18.