# Dataset Construction through Ontology-Based Data Requirements Analysis
## 5 Experiments and Analysis
### Discussion  
Taking an object detection system as an example, this paper elucidates the process of constructing data requirements based on target entities or their relationships. The initial step involves constructing the domain model for the given problem. While building this  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Dataset Distribution** & **mAP\_0.5** \\ \hline S1 & \(cov(req1,D1)=20\%,cov(req2,D1)=30\%\), & 0.7615 \\  & \(cov(req3,D1)=50\%\) & 0.7832 \\ S2 & \(cov(req1,D2)=40\%,cov(req2,D2)=40\%\), & 0.7832 \\  & \(cov(req3,D2)=20\%\) & 0.7963 \\ S3 & \(cov(req4,D3)=20\%\), \(cov(req2,D3)=30\%\), & 0.7963 \\  & \(cov(req3,D3)=50\%\) & 0.7398 \\ S4 & \(cov(req1+req5,D4)=40\%\), \(cov(req2+req5,D4)=40\%\), & 0.7398 \\  & \(cov(req3+req5,D4)=20\%\) & 0.7216 \\ S5 & \(cov(req1+req6,D5)=40\%\), \(cov(req2+req6,D5)=40\%\), & 0.7216 \\  & \(cov(req3+req6,D5)=20\%\) & 0.7216 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Recognition accuracy based on different dataset distributon.  
model, whether the problem can abstract semantic entities should be determined. Because it makes users understand the problem more easily and generates data requirements through semantic information. Meanwhile, it is imperative to consider potential relationships among entities and identify those entities directly related to the target entity. This careful consideration is essential as entities closely associated with the target entity exert a significant impact, ultimately influencing the detection performance aimed at the said target entity.  
For example, in a tumor recognition system, the identification of benign and malignant tumors is mainly influenced by factors such as the shape, texture, density, and size of the tumor. These features are typically used to establish domain models to achieve accurate identification and classification of tumors. Additionally, the likelihood of having a tumor is also influenced by independent factors such as the patient's age and gender, which are directly related to the tumor in the domain model. Therefore, it is necessary to focus more on the above-mentioned factors through the process of collecting, while factors such as color features, surrounding environmental features, and motion features, which do not have a significant impact on the identification results, do not need to be given too much attention.  
When handling ML systems that do not involve detection functionality, such as prediction and classification systems, the process of constructing requirements for all entity objects within the domain model can be executed. However, the effectiveness of this approach is heavily contingent on the computational performance of the machine learning system. Therefore, our subsequent research endeavors are directed towards minimizing the complexity associated with generating data requirements as much as possible. Moreover, in the case of sentiment analysis systems, because the sentiment analysis task primarily involves extraction of three elements--entity, aspect and opinion--from the text to analyze the positive or negative evaluations directed towards a specific object, we can construct the domain model based on these three elements [24]. The approach can even be considered for improving the quality of datasets for multi-modal systems [25; 26; 27]. By providing structured and standardized representations of domain knowledge, it is possible to combine different types of data such as text, images, and audio to unified entities and relationships in ontology to achieve the correlation and integration of different modal data.  
In fact, irrespective of the system type, as long as the problem domain allows for the extraction of multiple semantic entities, where entities have inherent aspects, and there are strong or weak relationships among these entities, the method proposed in this paper can be considered to enhance the quality of datasets used for training machine learning systems.