# SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
## 4 Experiments
### Evaluation of Actual Industrial KGs  
Table 7 shows the link prediction evaluation results on the industrial KG dataset. By comparison, we can see that our SFCA framework has improved by more than 13.11% (7.3 points) in the MRR indicators of different baselines on the fault diagnosis industrial dataset. Our SFCA achieves remarkable performance in industrial filed KGs. With this result, we believe our SFCA can play well in multi-field KGs.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**FB15K237**} & \multicolumn{6}{c}{**YAGO3-10**} \\ \cline{2-11}  & **MR** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MR** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline TransE & 175 & 0.331 & 0.234 & 0.369 & 0.526 & 1124 & 0.504 & 0.411 & 0.563 & 0.672 \\ TransE * & **110** & **0.371** & **0.275** & **0.410** & **0.563** & **648** & **0.535** & **0.452** & **0.583** & **0.688** \\ DistMult & 216 & 0.284 & 0.223 & 0.310 & 0.447 & 1723 & 0.130 & 0.084 & 0.135 & 0.219 \\ DistMult * & **145** & **0.320** & **0.240** & **0.346** & **0.482** & **1110** & **0.174** & **0.116** & **0.185** & **0.287** \\ ComplEx & 181 & 0.308 & 0.220 & 0.337 & 0.486 & 1118 & 0.198 & 0.131 & 0.214 & 0.325 \\ ComplEx * & **119** & **0.344** & **0.258** & **0.372** & **0.518** & **660** & **0.253** & **0.181** & **0.276** & **0.393** \\ RotatE & 177 & 0.336 & 0.240 & 0.374 & 0.530 & 1859 & 0.497 & 0.406 & 0.553 & 0.665 \\ RotatE * & **108** & **0.376** & **0.280** & **0.413** & **0.567** & **795** & **0.542** & **0.469** & **0.591** & **0.694** \\ HAKE & 184 & 0.343 & 0.246 & 0.380 & 0.537 & 1384 & 0.531 & 0.444 & 0.586 & 0.687 \\ HAKE * & **112** & **0.380** & **0.285** & **0.417** & **0.572** & **613** & **0.562** & **0.485** & **0.605** & **0.703** \\ \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**WN18 RR**} & \multicolumn{6}{c}{**NELL-995**} \\  & **MR** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MR** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline TransE & 5081 & 0.191 & 0.004 & 0.343 & 0.478 & 2238 & 0.239 & 0.079 & 0.348 & 0.508 \\ TransE * & **4209** & **0.221** & **0.029** & **0.382** & **0.500** & **353** & **0.325** & **0.202** & **0.394** & **0.548** \\ DistMult & 6043 & 0.406 & 0.358 & 0.437 & 0.490 & 2523 & 0.348 & 0.268 & 0.385 & 0.500 \\ DistMult * & **4935** & **0.414** & **0.364** & **0.444** & **0.503** & **472** & **0.398** & **0.311** & **0.435** & **0.564** \\ ComplEx & 6394 & 0.457 & 0.416 & 0.481 & 0.526 & 3664 & 0.369 & 0.289 & 0.405 & 0.518 \\ ComplEx * & **5274** & **0.463** & **0.421** & **0.488** & **0.535** & **727** & **0.417** & **0.331** & **0.453** & **0.582** \\ RotatE & 5648 & 0.466 & 0.435 & 0.480 & 0.527 & 2327 & 0.358 & 0.269 & 0.404 & 0.526 \\ RotatE * & **4597** & **0.473** & **0.440** & **0.488** & **0.539** & **337** & **0.405** & **0.313** & **0.450** & **0.580** \\ HAKE & 4132 & 0.492 & 0.450 & 0.509 & 0.574 & 2419 & 0.313 & 0.205 & 0.374 & 0.514 \\ HAKE * & **3325** & **0.504** & **0.463** & **0.519** & **0.585** & **348** & **0.373** & **0.270** & **0.427** & **0.567** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Link prediction results on four datasets. Bold numbers are the best results for each type of model. * denotes SFCA is used.