# Coarse-to-Fine Entity Alignment for Chinese Heterogeneous Encyclopedia Knowledge Base
## 4 Experiment
### Experiment Results  
In order to verify the effectiveness of the CFEA model, we conducted comparison experiments using several baselines. Moreover, we explored the effect of the number of topics parameter, i.e., the parameter \(K\) and LDA's threshold \(\omega\) on the model using CFEA with different values. The experimental results and analysis are shown below. Table 4 shows that the proposed method obtains competitive results on both datasets. For example, for the Baidu-Hudong dataset, our method outperforms other compared algorithms on both the F1 metric and the recall metric. As for the WiFi-Baidu dataset, our method performs the second best. The experimental results demonstrate the effectiveness of the proposed method. Moreover, Table 4 also shows that the two-stage models perform better than the single-stage models in most cases. This might be because more information (i.e., abstract and description) is used in these two-stage models. To analyze the influence of the number of topics on the model, we experimentally explored the change curve of the related results for different values of the parameter \(K\). The experimental results are shown in Figure 6. When \(K\) is set to 14, the model obtains the best \(F_{1}\) in Baidu-Hudong. When \(K\) is less than 14, the experimental results show that the precision is poor, the recall rate is high, but \(F_{1}\) is obviously poor. This might be because \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Baidu–Hudong**} & \multicolumn{3}{c}{**WiKi–Baidu**} \\ \cline{3-8}  & & **F1** & **Precision** & **Recall** & **F1** & **Precision** & **Recall** \\ \hline \multirow{8}{*}{single-stage methods} & ED & 0.8815 & 0.9695 & 0.8082 & 0.6039 & 0.5087 & 0.7429 \\  & weight ED & 0.9143 & 0.9714 & 0.8635 & 0.7581 & 0.7705 & 0.7460 \\  & LDA & 0.7637 & 0.7330 & 0.7971 & 0.4449 & 0.3425 & 0.6349 \\  & LCS & 0.9020 & 0.9716 & 0.8418 & 0.7199 & 0.8153 & 0.6444 \\  & weight LCS & 0.9062 & **0.9726** & 0.8482 & 0.7542 & 0.7265 & 0.7841 \\ \hline \multirow{8}{*}{two-stage methods} & weight ED+TFIDF & 0.9409 & 0.9697 & 0.9138 & 0.7586 & 0.7857 & 0.7333 \\  & weight LCS+TFIDF & 0.9388 & 0.9687 & 0.9106 & 0.7633 & 0.7147 & **0.8190** \\ \cline{1-1}  & weight LCS+LDA & 0.9432 & 0.9669 & 0.9206 & 0.8006 & 0.8045 & 0.7968 \\ \cline{1-1}  & WA-Word2 Vec & 0.8062 & 0.7437 & 0.8801 & 0.6590 & 0.8382 & 0.5429 \\ \cline{1-1}  & MED-Doc2 vec & 0.9464 & 0.9691 & 0.9248 & **0.8188** & **0.9073** & 0.7460 \\ \hline proposed method & CFEA & **0.9472** & 0.9680 & **0.9273** & 0.8099 & 0.8448 & 0.7778 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of \(F_{1}\), precision and recall in entity alignment. there are too few topics, and a topic will contain multiple layers of semantic information. At this time, the accuracy rate of the LDA model is low, because in the LDA model it is easy to misunderstand that some entity pairs have similar semantics in text but do not actually match, and so are matched anyway. When the \(K\) value is large, the accuracy of the model gradually increases, but the recall rate drops sharply. This might be because too many topics without obvious semantic information will make the matching of the topic model more stringent, that is, only very similar texts can be matched, and the rest will be discarded. The dataset Wikipedia-Baidu has a similar pattern, and the only difference is that the optimal \(K\) is set to 16. The impact of the similarity threshold of LDA on the overall model is given as follows. We change the threshold under the optimal parameter setting. Only the similarity scores that reach this threshold will be regarded as aligned, otherwise it will be judged as misaligned. The relevant experimental results are shown in Figure 7. The CFEA model obtains the best \(F_{1}\) score when the parameter \(\omega\) is 0.7. When the parameter is gradually increased from 0.1, the precision of the model gradually increases, but the recall is greatly reduced.