# Knowledge Graph Alignment Network with Node-Level Strong Fusion
## 5 Experiments
### Experimental Results
#### 5.4.1 DBP15K Results  
Table 2 shows the performance of different methods on the entity alignment task. The results of Hits@1 and Hits@10 are in percentage (%). Numbers in bold denote the best results among all models. The underline indicates the best effect in the comparison models. The experimental results show that SFEA significantly outperforms all baselines on DBP15K and further confirms the effectiveness of the proposed model. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Datasets**}} & \multirow{2}{*}{**Entities**} & \multicolumn{3}{c}{**DBP15K**} & \multirow{2}{*}{**Attributes**} & \multirow{2}{*}{**Rel.Triples**} & \multirow{2}{*}{**Attr.Triples**} & \multicolumn{1}{c}{**S-DBP15K**} \\  & & & & & & **Tri.Remain** \\ \hline \multirow{2}{*}{ZH-EN} & Chinese & 66,469 & 2830 & 8113 & 153,929 & 379,684 & 26\% \\  & English & 98,125 & 2317 & 7173 & 237,674 & 567,755 & 100\% \\ \multirow{2}{*}{JA-EN} & Japanese & 65,744 & 2043 & 5882 & 164,373 & 354,619 & 41\% \\  & English & 95,680 & 2096 & 6066 & 233,319 & 497,230 & 100\% \\ \multirow{2}{*}{FR-EN} & French & 66,858 & 1379 & 4547 & 192,191 & 528,665 & 45\% \\  & English & 105,889 & 2209 & 6422 & 278,599 & 576,543 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of DBP15K and S-DBP15K. (Rel.triples and Attr.triples stand for relations and attributes respectively. Tri.Remain in S-DBP15K Indicates the proportion of retained relation triples compared to DBP15K). This experiment contains some unmentioned entity alignment methods. Meta Relation Aware Entity Alignment (MRAEA) [22] is proposed as a meta-relations-aware entity alignment that directly models cross-language entity embedding, by focusing on the meta-semantics of the node's incoming and outgoing neighbors and their connected relation. [PERSON] et al. (HMAN) [23] applied GCNs to combine multi-aspect information of entities, and proposes two uses of a pre-trained multilingual BERT model to bridge cross-lingual gaps. Multiview highway graph convolutional network (MHGCN) [24] is employed as a highway graph convolutional network (GCN) for entity embedding in each view, weights and fuses the multiple views from each view. The Entity Visual Alignment (EVA) model [25] presents as an unsupervised approach and leverages visual similarities of entities to build a seed dictionary from scratch and expand the dictionary with iterative learning. The Structure and Semantics Preserving network (SSP) [26] jointly leveraged the global KG structure and local semantics preserving network for entity alignment. Specifically, among all TransE-based models, BootEA performs the best because it adopts a bootstrap strategy to iteratively expand the seed alignments. This indicates that the iterative strategy can significantly improve the performance of entity alignment. For GCN-based models, in only considering structure information model, NMN has better results than AliNet, because of a more efficient graph sampling strategy. Among the models considering relation information, MRAEA has better results than RDGCN, because it mines more relational hidden semantics. HMAN and AttrGNN both consider attribute information. AttrGNN considers different types of attribute information, achieves the best results in the baselines, which proves the importance of attribute information for entity alignment. However, the proposed SFEA model can achieve all the values of Hits@1 to be higher than 85%, those of Hits@10 higher than 93%, and those of MRR higher than 0.88. As far as the better performing baseline models HGCN, AttrGNN, MHGCN and EVA are concerned, although these baseline models also use different aspects of information, they use simple merging at the aspect level in the process of information fusion. The information is not fully utilized to obtain rougher entity representations. However, SFEA closely integrates information from different aspects, and makes use of the advantages of each type of information to achieve better entity alignment.