# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 5 Experiments and Results
### The Experimental Results  
#### 5.2.1 The Comparison of Six Baseline Methods  
We conducted comparative experiments on ZH-MSJ and ZH-ZHYSW. The experimental results of all the metrics are shown in Table 5. Most of the existing methods assume that the knowledge graph has abundant relationships among the entities. However, the absence of relationships between entities in the domain knowledge graph undermines the efficacy of the baselines and the accomplishment of the alignment task properly. Consequently, we utilized attribute triples to replace relationship triples in the learning process of the baselines, and denoted them with the symbol, \"+\". \"\(\uparrow\)\" and \"\(\downarrow\)\" indicates an increment or decrement in the baselines, which utilized attribute triples to replace relationship triples compared to themselves in Table 5. We could not execute MuGNN and HGCN directly, for they mainly utilize relationship triples for embedding, so we utilized \"--\" to represent their values in the metrics. Moreover, GCN-Align employs GCN to embed structural and attribute information for the entities, so GCN-Align+ is similar to GCN-Align in all the metrics. Table 5 suggests that the performance of DomainEA outperforms the baselines of almost all the metrics. The reasoning for that is, in the filtering module, DomainEA can effectively exclude noisy entities and select a few candidate entities before KG embedding. In the embedding module, we employed the SA to generate the high-order local features, and introduce attention to the dynamically calculated weights between the entities and attribute values in the AA. These strategies enhanced the efficacy of entity alignment. However, in comparison to the other baselines, MuGNN+ and HGCN+ only rely on structural information for embedding, while ignoring the significance of entity attribute values on \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Hits@1 (\%)** & **Hits@10 (\%)** & **Hits@50 (\%)** & **MRR (\%)** \\ \hline GCN-Align & 46.25 & 86.74 & 92.21 & 60.07 \\ MuGNN & —— & —— & —— & — \\ HGCN & —— & —— & — & — \\ FGWEA & 55.94 & 88.79 & 92.36 & 69.45 \\ PEEA & 53.59 & 88.61 & 90.16 & 67.98 \\ SDEA & 55.18 & 89.26 & 91.65 & 70.94 \\ GCN-Align+ & 47.03 (0.78\(\uparrow\)) & 86.13 (0.61\(\downarrow\)) & 92.73 (0.52\(\uparrow\)) & 60.41 (0.34\(\uparrow\)) \\ MuGNN+ & 53.71 & 92.48 & 96.74 & 69.09 \\ HGCN+ & 59.02 & 95.38 & 97.19 & 73.23 \\ FGWEA+ & 62.14 (6.2\(\uparrow\)) & 97.24 (**8.45\(\uparrow\)**) & **99.02** (6.66\(\uparrow\)) & 76.61 (**7.16\(\uparrow\)**) \\ PEEA+ & 60.39 (6.8\(\uparrow\)) & 93.87 (5.26\(\uparrow\)) & 98.71 (**8.55\(\uparrow\)**) & 74.63 (6.65\(\uparrow\)) \\ SDEA+ & 62.98 (**7.8\(\uparrow\)**) & 97.45 (8.19\(\uparrow\)) & 98.15 (6.5\(\uparrow\)) & 78.77 (6.83\(\uparrow\)) \\ DomainEA & **64.66** & **98.07** & 98.91 & **79.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experiments. the alignment tasks, which results in the loss of semantic information about the entities. Although GCN-Align+ jointly embeds both the structural and attribute information of the KGs, as domain-specific KGs contain abundant interference information, GCN-Align+ cannot effectively address this problem, which leads to a suboptimal alignment performance. PEEA+ relies on a limited number of anchor links for training, but it is a challenge to obtain high-quality anchored links in recipes, so the performance of PEEA+ may be limited. FGWEA+ mainly utilizes structural information to optimize entity semantics and knowledge graph structure. However, in domain knowledge graphs, the lack of structural information makes us have to utilize attribute triples to replace the relationship triples, which limits the effectiveness of FGWEA+. SDEA+ demonstrates sub-optimal performance across all the metrics. A thorough analysis of its limitations reveals that it employs a transformer to extract semantic information from the entity attributes, and utilizes a GRU with an attention mechanism to capture the structural information of entities.