## 4 Experiment
### Results
In this case, the average prediction results are not be sufficiently representative; thus, we carried out knowledge graph completion tasks for each relationship. Table 6 shows the average evaluation results (MRR) for each relationship. From the above experiments, it can be seen that the CSEA model achieves good results in all tasks, indicating that it can help greatly in expressing and inferring knowledge. The increase in MRR indicates that the knowledge expression learned by CSEA is more universal. The increase in Hits@K indicates that the CSEA model has a higher recommendation quality for knowledge graph completion tasks. Rot-Pro and HousE are effective models based on projections and rotations; Rot-Pro mainly implements projection and rotation in complex spaces, while HousE does so in higher dimension spaces. However, the experimental results show that there are limitations to a single operation. Our CSEA ensemble model can make more comprehensive use of information. For the overall task, CSEA adopts ensemble learning to further reduce the fluctuations of information, achieving good average results. In the relationship evaluation task, certain entity types appear more frequently in the cybersecurity knowledge graph, meaning that the relationship types \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **InstanceOf** &
\begin{tabular}{c} **Observed** \\ **Example** \\ \end{tabular} & **PeerOf** & **AttackTo** & **TargetOf** & **CanFollow** & **CanPrecede** & **ChildOf** & **ParentOf** \\ \hline TransE [22] & 0.662 & 0.668 & 0.194 & 0.820 & 0.827 & 0.692 & 0.811 & 0.706 & 0.725 \\ DistMult [33] & 0.693 & 0.683 & 0.638 & 0.781 & 0.802 & 0.634 & 0.730 & 0.606 & 0.647 \\ ComplEx [34] & 0.694 & 0.683 & 0.553 & 0.777 & 0.794 & 0.734 & 0.827 & 0.752 & **0.784** \\ HAKE [30] & 0.689 & 0.679 & **0.655** & 0.810 & 0.821 & 0.742 & 0.848 & 0.753 & 0.778 \\ Rot-Pro [53] & 0.697 & 0.681 & 0.624 & **0.822** & **0.835** & 0.744 & **0.860** & 0.750 & 0.775 \\ HousE [31] & 0.699 & 0.688 & 0.614 & **0.822** & 0.832 & **0.746** & 0.856 & **0.754** & 0.780 \\ CSEA & **0.721** & **0.713** & 0.646 & 0.819 & 0.830 & 0.739 & 0.850 & 0.751 & 0.773 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average evaluation results (MRR) for each relationship; results in bold are the best. \begin{table}
\begin{tabular}{l l l c c c c} \hline \hline
**Prediction** & **Model** & **MRR** & **MR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline \multirow{6}{*}{Head Entity \(<\)2, r, b-} & TransE [22] & 0.713 & 302 & 0.674 & 0.734 & 0.777 \\  & DistMult [33] & 0.690 & 623 & 0.638 & 0.738 & 0.760 \\  & Complex [34] & 0.735 & 670 & 0.720 & 0.745 & 0.760 \\  & HAKE [30] & 0.740 & 400 & 0.725 & 0.750 & 0.769 \\  & Rot-Pro [53] & 0.743 & 373 & 0.728 & 0.753 & 0.769 \\  & HousE [31] & 0.747 & 309 & 0.731 & 0.754 & 0.774 \\  & CSEA & **0.756** & **147** & **0.735** & **0.764** & **0.794** \\ \hline \multirow{6}{*}{Tail Entity \(<\)h, r, \(>\)} & TransE [22] & 0.713 & 313 & 0.672 & 0.733 & 0.777 \\  & DistMult [33] & 0.689 & 621 & 0.640 & 0.732 & 0.759 \\ \cline{1-1}  & ComplEx [34] & 0.735 & 677 & 0.719 & 0.745 & 0.765 \\ \cline{1-1}  & HAKE [30] & 0.742 & 422 & 0.729 & 0.747 & 0.770 \\ \cline{1-1}  & Rot-Pro [53] & 0.746 & 382 & 0.731 & 0.754 & 0.770 \\ \cline{1-1}  & House [31] & 0.748 & 319 & 0.732 & 0.753 & 0.776 \\ \cline{1-1}  & CSEA & **0.756** & **148** & **0.736** & **0.765** & **0.793** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average evaluation results in predicting head and tail tasks; results in bold are the best. connected with the above entities appear more frequently as well. It can be observed that when there are substantial amounts of relational data, many single models have a propensity to fall into a local optimal solution; our results show that CSEA is better able to avoid falling into a local optimal solution in scenarios involving large-scale data. Even with insufficient amounts of data, CSEA is able to maintain its overall performance via ensemble Learning.