# Dataset Construction through Ontology-Based Data Requirements Analysis
## 5 Experiments and Analysis
### Experiments Results
and an equal number of data that satisfy the data requirements derived from relation criterion as training datasets, respectively. If the training dataset satisfies the data requirement \((building,behind,sign)\), the recognition accuracy of the image recognition system for traffic signs will decrease compared to the system trained on the dataset that satisfies the triple \((tree,behind,sign)\). * **Experiment 3a and 3b:** In experiments 3a and 3b, we filtered the candidate dataset to obtain 1768 data that do not satisfy the data requirements and an equal number of data that satisfy the data requirements derived from relation criterion as training dataset, respectively. The result shows that if the training dataset satisfies \((tree,above,sign)\), \((building,above,sign)\) and \((car,above,sign)\) at the same time, the recognition accuracy of the detection system for traffic signs is higher than in dataset without the data requirements. * **Experiment 4a and 4b:** In experiments 4a and 4b, we filtered candidate datasets to obtain 3260 data that do not satisfy the data requirements and an equal number of data that satisfy the data requirements derived from relation criterion as the training dataset, respectively. The result shows that if the training dataset satisfies \((car,behind,sign)\) and \((car,under,sign)\) at the same time, the recognition accuracy of the detection system for traffic signs is higher than in the dataset without the data requirements. We trained the model using datasets of the same size but in different experimental conditions. The results show that the model trained by a dataset that satisfies the data requirements has higher predictive ability compared to the model trained by a dataset that does not satisfy the data requirements. Next, we conducted another experiment to demonstrate the difference in the model's performance, where the sizes of training datasets are different. Table 7 shows the different sizes of the datasets before and after satisfying the data requirements and Figure 9 shows the predictive performance of models trained by different datasets based on different data requirements. In experiment (a), the dataset satisfies data requirements of \((tree,above,sign)\), \((building,above,sign)\) and \((car,above,sign)\) and is \begin{table}
\begin{tabular}{c l} \hline \hline
**Id** & **Data Requirements** \\ \hline \(req1\) & \(\{(\text{color, }red), (\text{shape, }circle), (\text{size, }medium))\} \\ \(req2\) & \(\{(\text{color, }red), (\text{shape, }circle), (\text{size, }large)\}\) \\ \(req3\) & \(\{(\text{color, }red), (\text{shape, }square), (\text{size, }small)\}\) \\ \(req4\) & \(\{(\text{color, }red), (\text{shape, }square), (\text{size, }medium)\}\) \\ \(req5\) & \(\{(\text{color, }red), (\text{shape, }square), (\text{size, }large)\}\) \\ \(req6\) & \(\{(\text{color, }red), (\text{shape, }triangle), (\text{size, }small)\}\) \\ \(req7\) & \(\{(\text{color, }red), (\text{shape, }triangle), (\text{size, }medium)\}\) \\ \(req8\) & \(\{(\text{color, }red), (\text{shape, }triangle), (\text{size, }large)\}\) \\ \(req9\) & \(\{(\text{color, }blue), (\text{shape, }circle), (\text{size, }small)\}\) \\ \(req10\) & \(\{(\text{color, }yellow), (\text{shape, }square), (\text{size, }large)\}\) \\ \(req11\) & \((building,behind,sign)\) \\ \(req12\) & \((tree,above,sign)\) \\ \(req13\) & \((building,above,sign)\) \\ \(req14\) & \((sign,above,street)\) \\ \(req15\) & \((pedestrian,behind,sign)\) \\ \(req16\) & \((car,above,sign)\) \\ \(req17\) & \((car,under,sign)\) \\ \(req18\) & \((car,behind,sign)\) \\... &... \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data Requirements Derived From Attribute Relation Criterion. smaller than the baseline dataset. In experiment (b), the dataset satisfies data requirements of \(car,behind,sign)\) and \((car,under,sign)\) and is smaller than the benchmark dataset.