## 5 Experiment
### Case Study  
In Table 8, we show how the TSET model could predict the SPARQL query more accurately by demonstrating two examples of question-SPARQL pairs sampled from three datasets. Within these examples, T5 gives the wrong prediction in which a triple flip occurs. For example, in the second question, the user asks, \"_What is the IQ test for insights measurements?_\" After Entity Linking and Relation Linking, the identified entities are as follows: intelligence wd:Q83500; IQ test wd:Q12021385; instance of wdt:P31; measures wdt:P2575. Both models correctly identify the second triple (?x wdt:P31 wd:Q12021385), indicating that the answer must be a type of IQ test. However, for the first triple, T5 fails to make the correct prediction, resulting in a Triple Flip error. In contrast, our proposed TSET model effectively captures the structural and positional information within the triple, giving the correct triple prediction (?x wdt:P2575 wd:Q83500). This indicates that it should be a subject measuring intelligence rather than intelligence measuring something. The TSET model demonstrates its ability to grasp the internal structure and positional information of triples, surpassing T5 in accurately generating SPARQL queries.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Question \#1 (LC-QuAD 2.0)** & **How Many Exemplars of the New Treatment Are There?** \\ \hline Entities \& Relations & New Testament wd:Q18813; exemplar of wdt:P1574 \\ \hline T5 & SELECT (COUNT (?vot) AS?vrt) \\  & [wd:Q18813 wdt:P1574 wrt.) \\ \hline TSET & SELECT (COUNT (?vot) AS?vrt) \\  & [vrvot) wdt:P1574 wd:Q18813.] \\ \hline
**Question \#2 (LC-QuAD 2.0)** & **What is the IQ test for insights measurements?** \\ \hline Entities \& Relations & intelligence wd:Q83500; IQ test wd:Q12021386; \\  & instance of wdt:P31; measures wdt:P2575 \\ \hline T5 & SELECT DISTINCT?x WHERE \\  & [wd:Q83500 wdt:P2575 x.wdt:P31 wd:Q12021385] \\ \hline TSET & SELECT DISTINCT?x WHERE \\  & [wd:P25750 wd:Q83500. \\  & [wd:P31 wd:Q12021385] \\ \hline
**Question \#3 (QALD-9 plus)** & **Who wrote Harry Potter?** \\ \hline Entities \& Relations & [PERSON] wd:Q8337; author wdt:P50 \\ \hline T5 & SELECT DISTINCT?x WHERE \\  & [x wdt:P50 wd:Q8337.] \\ \hline TSET & SELECT DISTINCT?x WHERE \\  & [wd:Q83837 wd:P50 x.] \\ \hline
**Question \#4 (QALD-9 plus)** & **How many awards has [PERSON]?** \\ \hline Entities \& Relations & [PERSON] wd:Q33760; award received wdt:P166 \\ \hline T5 & SELECT (COUNT (DISTINCT?var0) AS?var1) WHERE \\  & [var0 wdt:P166 wd:Q33760.] \\ \hline TSET & SELECT (COUNT (DISTINCT?var0) AS?var1) WHERE \\  & [wd:Q33760 wdt:P166 wart.] \\ \hline
**Question \#5 (QALD-10)** & **Is fafahan a big city?** \\ \hline Entities \& Relations & Isfahan wd:Q42058; instance of wdt:P31; big city wd:Q1549591 \\ \hline TS & ASK [wd:Q1549591 wdt:P31 wd:Q42058.] \\ \hline TSET & ASK [wd:Q42058 wdt:P31 wd:Q154991.] \\ \hline
**Question \#6 (QALD-10)** & **When did human first start to bouldering?** \\ \hline Entities \& Relations & bouldering at:Q82989; date of establishment wdt:P571 \\ \hline T5 & SELECT DISTINCT?x WHERE \\  & [x wdt:P571 wd:Q85298] \\ \hline TSET & SELECT DISTINCT?x WHERE \\  & [wd:Q82989 wdt:P571 x. ] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Some examples on three test datasets. In the SPARQL query, keywords are highlighted in blue and bold, while differences in subject/object are highlighted in red. Our model gives all correct predictions in these cases, while the original T5-small model fails.