# Dataset Construction through Ontology-Based Data Requirements Analysis
## 3 Ontology-Based Approach to Data Requirements Analysis
### Criteria-Based Data Requirements Specification
We combine the corresponding values of each attribute to form the pair of \((a,v)\), such as \(\{(color,\,\text{red}),(shape,\,\text{circle})\}\). After obtaining a series of entities directly related to \(trafficSign\) and their attributes with values, we can obtain all the data requirements based on \(req^{attr}_{o}\) as shown in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Crucial Entity** & **Attribute** & **Value** & \(\mathbf{Req^{attr}_{o}}\) \\ \hline \multirow{4}{*}{\(TrafficSign\)} & \multirow{2}{*}{shape} & circle & \(\{(shape,\,\text{circle}),(color,\,\text{red}),(leaf,\,\text{flourish})\}\), \\  & & square & \(\{(shape,\,\text{circle}),(color,\,\text{red}),(leaf,\,\text{withered})\}\), \\ \cline{3-4}  & & red & \(\{(shape,\,\text{circle}),(color,\,\text{blue}),(leaf,\,\text{flourish})\}\), \\  & & yellow & \(\{(shape,\,\text{circle}),(color,\,\text{blue}),(leaf,\,\text{withered})\}\), \\ \hline \multirow{2}{*}{_Tree_} & \multirow{2}{*}{leaf} & flourish & \(\{(shape,\,\text{square}),(color,\,\text{red}),(leaf,\,\text{flourish})\}\), \\  & & withered & \(\{(shape,\,\text{square}),(color,\,\text{red}),(leaf,\,\text{withered})\}\), \\ \end{tabular}
\end{table} 
Table 1: Data requirements based on _Attribute Coverage Criterion_ towards ontology of traffic scene. **Relation Coverage Criterion. ** Given a domain ontology \(O\), a set of crucial entity concepts \(E^{{}^{\prime}}_{O}\) and the entity nodes directly related to an entity in \(E^{{}^{\prime}}_{O}\), for each possible entity combination of pair \((e_{o1},e_{o2})\) and the value of relation concept \(r\) related to \(e_{o1}\) and \(e_{o2}\), there must exist at least one data point in the dataset to cover it. Relation Coverage Criterion aims to determine data requirements by covering multiple entities with their relations. The data requirements generated based on the Relation Coverage Criterion need to be formulated as follows:  
\[req^{rel}_{o}=\{(e_{o1},v,e_{o2})|v\in F_{val}(r)\wedge r\in F_{rel}(e_{o}) \wedge(e_{o1},e_{o2})\in E^{{}^{\prime}}_{O}\cap F_{entity}(e)\} \tag{8}\]  
where \(F_{val}(r)\) represents all the value nodes directly related with relation node \(r\) and the combination is denoted as a triple \((e_{o},v,e_{o})\). As shown in Figure 2, we can see that the relation node _Position_ is linked with the target entity node _TrafficSign_ and entity nodes linked with _Position_, such as _Tree, Vehicle, Pedestrian_, and _TrafficLight_. We can obtain all the data requirements in the form of three-triples about the entity _trafficSign_ based on \(req^{rel}_{o}\) as shown in Table 2.  
It should be noted that the shape of a red-colored sign is generally circular in reality, but it may also appear in different combinations due to factors such as lighting, weather, and the occlusion of obstacles. During the phase of data requirement construction, we only need to combine the values of attributes. Whether the combination is in line with the actual situation needs the participation of domain experts. Since the criteria are formalized, the algorithm for generating corresponding data requirements based on criteria is summarized in Algorithm 1. The provided example semantic model is only partial. As the relevant field evolves, new domain-related elements may need to be introduced. Analysts and domain experts should maintain these semantic models to ensure that they provide up-to-date and advanced information for specific model construction. Next, we will conduct experiments based on criteria to illustrate the mapping relation between the recognition accuracy of semantic objects in images and their corresponding system. The research aims to provide engineers with a foundation for describing the learning behavior of ML systems. Specifically, it aims to identify which features of data lead to the improvement or decline in the capability of the system.