## 5 Experimental Section
### Analysis and Discussion
#### 5.3.4 Case Study  
Table 9 shows the case study of our proposed ERHGA. It can be seen from the first and second examples that our proposed model has an outstanding extraction of SEO and EPO. In the third example, our model identified the relation between \"Azerbaijan\" and \"Rasizade\" as \"leaderName\". According to the annotation of the original dataset, the relation between them should be \"leader\". However, in the second example, our model can accurately distinguish \"leader\" and \"leaderName\". We found that \"leaderName\" contains more samples than \"leader\" by revisiting the original dataset. The class imbalance in the dataset itself will affect the performance of our model. In the fourth example, our model recognized the relational triples (Lanka, currency, Rupee) and (Hall, location, Haputale). Although these triples are not marked in the dataset, through reading this example, the semantics of the sentence actually express these two relational triples. As analyzed in Section 5.1.1., the annotated triples of the dataset are not completely accurate. This shows that our model can understand semantics better, is more in line with human cognition, and has a certain generalization ability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{3}{c}{**ERHGA**} & \multicolumn{3}{c}{**PRGC**} \\ \cline{2-7}  & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** \\ \hline E1 & 98.9 & 95.1 & 96.9 & 69.4 & 96.3 & 80.7 \\ E2 & 98.2 & 94.4 & 96.3 & 72.1 & 95.7 & 82.2 \\ R & 97.1 & 93.4 & 95.2 & 92.8 & 96.2 & 94.5 \\ (E1, R) & 95.4 & 92.6 & 94.0 & - & - & - \\ (R, E2) & 96.1 & 93.0 & 94.5 & - & - & - \\ (E1, E2) & 96.1 & 93.6 & 94.8 & 96.0 & 92.1 & 94.7 \\ (E1, R, E2) & 94.3 & 92.3 & 93.3 & 94.0 & 92.1 & 93.0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results (%) of relational triple elements on ERHGA and PRGC.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Sentences**} & \multicolumn{1}{c}{**Ground Truth**} & \multicolumn{1}{c}{**Predictive Value**} \\ \hline Sri Jayawardenepura Kotte is the capital of & (Hospital, country, Lanka), & (Hospital, country, Lanka), \\ Sri Lanka, where Ampara Hospital is located. & (Lanka, capital, Kotte) & (Lanka, capital, Kotte) \\ \hline Ampara Hospital is in Eastern Province, Sri & (Hospital, country, Lanka), & (Hospital, country, Lanka), \\ Lanka. [PERSON] is a & (Lanka, leaderName, [PERSON]), & (Lanka, leaderName, [PERSON]), \\ leader there. & (Hospital, state, Lanka) & (Hospital, state, Lanka) \\ \hline Baku is the capital of Azerbaijan and the & & \\ leader of the legislature (the National & (Azerbaijan, capital, Baku) & (Azerbaijan, capital, Baku) \\ Assembly) is [PERSON]. Azerbaijan is & (Azerbaijan, leader, [PERSON]) & (Azerbaijan, leaderName, [PERSON]) \\ the location of the Baku Turkish & & \\ Martyrs memorial. & & \\ \hline Adisham Hall is located in Haputale, Sri & (Lanka, capital, Kotte) & (Lanka, currency, Rupee) \\ Lanka. The capital of Sri Lanka is Sri & (Hall, country, Lanka) & (Hall, country, Lanka) \\ Jayawadenepura Kotte, the language used in the country is Tamil and the currency is & (Lanka, language, language) & (Lanka, language, language) \\ the Sri Lankan Rupee. & & (Hall, location, Haputale) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Case study for ERHGA.