# Dataset Construction through Ontology-Based Data Requirements Analysis
## 5 Experiments and Analysis
### Experiments Results
The results indicate that despite the size of the dataset satisfied with the data requirements being smaller than the baseline dataset, the training performance is better than the initial status. The previous experiment demonstrated how the recognition capability of the system changes when a dataset contains data that fully satisfies the data requirements and data that fail to satisfy the requirements. Next, we would like to quantitatively compare how the proportion of data satisfying different requirements in the dataset affects the results. We use coverage rate to divide the candidate dataset based on whether satisfy the criteria, given a dataset \(D\) and a data requirement _req_ satisfy the criteria, the coverage rate of _req_ is the number of items in \(D\) that matches _req_. Mathematically, the coverage rate is defined as follows:  
\[cov(L,D)=|\{t\in D\mid M(t,req)=1\}| \tag{9}\] \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Exp.** & \multicolumn{2}{c}{**Datasets Satisfy**} & \multirow{2}{*}{**Data Size**} & \multirow{2}{*}{**mAP\%50**} \\  & & **the Data Requirements** & & \\ \hline Constructing Datasets & 1a & - & 321 & 0.7748 \\ Randomly (Base Case) & 2a & - & 1316 & 0.8717 \\  & 3a & - & 1768 & 0.8695 \\  & 4a & - & 3260 & 0.7754 \\ Constructing Datasets & 1b & _req_5 & 321 & 0.8018 \\ Using Data Requirements & 2b & _req_15 & 1316 & 0.9015 \\  & & _req_16 & & \\  & 3b & _req_17 & 1768 & 0.8712 \\  & & _req_20 & & \\  & 4b & _req_21 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: YOLO performance report for detecting label _Sign_ before and after constructing dataset using data requirements. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Exp.**} & \multicolumn{1}{c}{**Base Case**} & \multicolumn{1}{c}{**Satisfying Data Requirements**} \\ \hline Experiment 3. & 1768 & 1300 \\ Experiment 4. & 3260 & 2500 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The size of dataset before and after satisfying the data requirements. Figure 9: The mAP\_0.5:0.95 scores of model trained by candidate dataset and smaller size dataset constructed by data requirements. (**a**) comparison of dataset satisfies the data requirements mentioned in Experiment 3 and candidate dataset. (**b**) comparison of dataset satisfies the data requirements mentioned in Experiment 4 and candidate dataset. where \(M(t,req)=1\) means an item matches the \(L\). When \(D\) is known, we can simplify \(cov(req,D)\) with \(cov(req)\), we would like each data in the \(D\) to match the criterion or we just want the rate to be higher than a threshold \(\xi\). We denote \(S(D)\) as the state model obtained by the ML system after training with a specific dataset, and \(S(D^{\prime})\) as the state model obtained by the ML system after training with a different dataset filtered by the value of attribute nodes or the combination of entity ontology nodes and relation ontology nodes based on _Attribute Coverage Criterion_ and _Relation Coverage Criterion_. The candidate datasets are divided into different subsets based on the coverage criteria and their corresponding distribution rates. Each subset represents a subsystem with specific prediction abilities. Table 8 illustrates the performance of subsystems that satisfy the different coverage criteria distributions. For example, when the percentage of subsets satisfied with coverage combinations \(req1\) increases from 20% to 40%, and the percentage of subsets satisfied with coverage combinations \(req2\) increases from 30% to 40%, while the percentage of subsets satisfied with coverage combinations \(req3\) decreases from 50% to 20%, the subsystem evolves from \(S1\) to \(S2\), which exhibits different predictive behaviors. In this case, the accuracy of \(S2\) in predicting red and round traffic signs slightly improves compared to \(S1\). Similarly, when the subsets change from being satisfied with the combination of \(req1+req5\) to being satisfied with the combination of \(req1+req6\), the subsystem evolves from \(S4\) to \(S5\).