# SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
## 5 Conclusions  
Motivated by the formal concept analysis theory, we propose a novel scalable formal concept-driven knowledge graph completion framework (SFCA) applying to multiple verticals. SFCA can automatically generate formal concepts from KG with a coarse-to-fine extraction strategy and a formal concept-supervised link prediction module to filter candidate entities from the perspective of formal concepts. Experiments on five public datasets demonstrate the effectiveness and scalability of SFCA. In addition, our model is also experimentally performed on real industrial datasets to demonstrate that the model has high performance in both general and industrial domains. Despite the performance of SFCA, it is still some areas for improvement. First, the application of our method on the KGC task is based on the closed-world hypothesis, which considers any triplet not explicitly present in the graph as a negative triplet. Second, our method only applies to the KGC task and does not extend to other knowledge-related tasks. Thus, our further work is to study the application of formal concept analysis on KGC tasks under the open-world hypothesis and explore the application of formal concept analysis on various concept-related KG tasks, such as life-long learning. Third, this paper's knowledge graph embedding-related tasks only involve knowledge graph completion. Future work can consider other knowledge map-related tasks such as named entity recognition and relationship extraction, that is, using formal concept analysis to assist tasks such as named entity recognition and relationship extraction. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Model**}} & \multicolumn{6}{c}{**FB15K237**} & \multicolumn{6}{c}{**NELL-995**} & \multicolumn{6}{c}{**Dbpedia-242**} \\ \cline{2-13}  & **MR** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MRR** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MR** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline TransE + U & 167 & 0.309 & 0.215 & 0.340 & 0.496 & 1261 & 0.249 & 0.153 & 0.288 & 0.428 & 1332 & 0.281 & 0.123 & 0.393 & 0.538 \\ TransE + S & 172 & 0.303 & 0.208 & 0.337 & 0.494 & 1212 & 0.255 & 0.156 & 0.295 & 0.436 & 1302 & 0.302 & 0.132 & 0.427 & 0.573 \\ TransE + C & 177 & 0.295 & 0.199 & 0.330 & 0.484 & 485 & 0.310 & 0.187 & 0.374 & 0.532 & 901 & 0.331 & 0.159 & 0.461 & 0.596 \\ TransE + U \({}^{*}\) & 29 & **0.756** & **0.709** & 0.779 & **0.850** & 232 & 0.344 & 0.248 & 0.386 & 0.527 & 635 & 0.316 & 0.170 & **0.415** & **0.557** \\ TransE + S & 28 & 0.754 & 0.704 & **0.780** & 0.849 & **226** & **0.351** & **0.253** & **0.392** & **0.536** & **0.606** & **0.336** & **0.182** & 0.446 & 0.586 \\ RotatE + U & 188 & 0.316 & 0.221 & 0.351 & 0.507 & 1389 & 0.342 & 0.249 & 0.379 & 0.522 & 1935 & 0.337 & 0.197 & 0.434 & 0.560 \\ RotatE + S & 190 & 0.324 & 0.230 & 0.357 & 0.514 & 1357 & 0.351 & 0.260 & 0.385 & 0.553 & 1788 & 0.372 & 0.239 & 0.464 & 0.592 \\ RotatE + C & 192 & 0.318 & 0.226 & 0.348 & 0.503 & 503 & 0.404 & 0.305 & 0.455 & 0.593 & 1049 & 0.419 & 0.310 & 0.486 & 0.605 \\ RotatE + U \({}^{*}\) & 34 & **0.761** & **0.712** & **0.784** & 0.853 & 223 & **0.433** & **0.343** & 0.469 & **0.611** & **772** & **0.356** & **0.236** & **0.454** & 0.576 \\ RotatE + S \({}^{*}\) & **32** & 0.760 & 0.711 & **0.784** & **0.855** & **220** & 0.433 & 0.341 & **0.470** & **0.611** & **718** & 0.413 & 0.299 & 0.485 & **0.607** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Link prediction results on three datasets. Bold numbers are the best results for each type of model. \({}^{*}\) Denotes SFCA is used. +U denotes uniform sampling strategies are used. +S denotes self-adversarial negative sampling strategies used. +C denotes Commonsense-Aware Knowledge Embedding framework used.