# A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks
## 5 Experiments and Results
### The Experimental Results
#### 5.2.2 Ablation Experiments  
For a comprehensive evaluation of our approach, we conducted three comparative experiments. Respectively, the first experiment was to compare the three deep-learning blocking methods. These methods have different tuple embedding solutions in the filtering module, and these solutions, respectively, are autoencoder, CTT, and hybrid. The second experiment was to evaluate DomainEA and DomainEA-f; DomainEA-f refers to an entity alignment method which does not have a filtering module compared to DomainEA. The third experiment was to evaluate the effect of DomainEA with various hidden layers in the SA. The primary objective was to verify the necessity of implementing the filtering module and to investigate the influence of the number of hidden layers in the SA on the experimental outcomes. In the filtering module, we employed self-supervision deep-learning blocking to exclude noisy entities and generate the candidate sets for each entity. Blocking consisted of pre-trained word embedding, tuple embedding, and cosine pairing. We selected three representative solutions in the tuple embedding: autoencoder, CTT, and hybrid, which achieve higher recall and smaller candidate sets on the various datasets. These tuple embedding solutions are shown as follows:  
* Autoencoder: The autoencoder model consists of an aggregator, encoder, and decoder. The aggregator can dispose of the various sequences of word embedding, which the feed-forward NN cannot accept. The encoder and decoder utilize two-layer feed-forward NNs with the Tanh activation function to reconstruct the feature of the entity. * CTT: The CTT model consists of an aggregator, Siamese summarizer, and classifier. CTT exploits information in multiple tuples. It perturbs the tuples in Tables 1 and 2, and generates synthetic labeled data, which can train a deep-learning model to produce embeddings for each tuple. * Hybrid: The hybrid model consists of an autoencoder and CTT, which are stacked by training the autoencoder first and then the CTT. This method employs the trained encoder of the autoencoder as the aggregator for CTT to generate the tuple embeddings. We conducted an evaluation of the recall of the three methods above on various candidate set sizes. The results, presented in Table 6, indicate that the autoencoder almost always achieved the highest recall on multiple candidate sets, compared to the CTT and hybrid. Moreover, the hybrid model, respectively, achieved the highest recall on 1.41 and 2.53, and both autoencoder and CTT achieved the highest recall on 5.62. In general, all three methods had high recall on small candidate set sizes. Therefore, our approach selected autoencoder as the tuple embedding for the filtering module. We conducted a comparative experiment on DomainEA-f and DomainEA. Through this experiment, we could evaluate the effectiveness of the filtering module. Table 7 reveals that DomainEA outperformed DomainEA-f on all the evaluation metrics and has superior runtime efficiency. Specifically, DomainEA, respectively, achieved improvements of 6.27%, 2.34%, 1.25%, and 7.88% in Hits@1, Hits@10, Hits@50, and MRR, while reducing the runtime by 43.04 s when compared to DomainEA-f. The result shows that DomainEA can effectively exclude noisy entities and greatly simplify the calculation process. We conducted an experiment with various numbers of hidden layers of the SA and investigated the impact of the number of hidden layers on our approach. The experiment results are shown in Table 8.  
We can infer from the experimental results in Table 8 that increasing the number of hidden layers in the SA has a positive impact on all the evaluation metrics, but does not mean better performance will be achieved. In particular, the 1-layer SA received the lowest scores on all the metrics compared to the others. Except for the 4-layer SA, which recorded the highest score in Hits@50, the 3-layer SA achieved the highest scores on the