# Dataset Construction through Ontology-Based Data Requirements Analysis
## 4 The Supporting Tool  
It is essential to determine the candidate dataset and the data types, which can be numerical, textual, or image-based. Additionally, it is important to identify the fields that need to be extracted from the dataset. If the data type is image-based, the semantic information in images needs to be converted into text or numerical form for comparison with the constructed data requirements. Based on the data requirements, we can analyze and filter the existing candidate dataset. Items in the candidate dataset that do not satisfy the data requirements will be documented in a missing report. This report will serve as a reference for future stages, where these missing items can be supplemented and compensated for. This process ensures that the dataset is comprehensive and accurate, providing a solid foundation for analysis and decision-making. Our supporting tool primarily provides the function of image-based dataset construction, which is mainly divided into three components, as shown in Figure 3.  
First is the domain model analysis module, which can standardize the domain model files into a row data list composed of multiple concepts. The second is the data requirements specification module, which generates a series of data requirements based on criteria. The third is the dataset construction module, where the candidate dataset is filtered to determine if it satisfies the output of the data requirement specification modules. The result of filtering and data requirement construction can guide the generation of missing reports to address any deficiencies in the dataset within this module. This module is implemented differently depending on the system task, and we have specifically constructed an image dataset for image recognition tasks in traffic scenarios. The user interface is mainly developed based on these three modules and provides image display functionality, which consists of a main image and several thumbnails. Users can switch to display different images by clicking on the thumbnails, and they can also switch between displaying the original image and the annotated image by clicking on buttons. To extract semantic information from image data, we use an anchor-based object detection technique proposed in the field of computer vision. The detector calculates the probability of each anchor box generated at the beginning of the procedure. By using standard metrics, such as Intersection over Union (IoU), which measures the overlap between the anchor box and the ground truth bounding box the model continuously adjusts the initial anchor boxes to finally obtain the optimal predicted bounding box. For instance, Figure 4 illustrates a set of detected objects and their predicted bounding boxes for label prediction. Finally, the recognition accuracy towards objects is calculated based on metrics such as mean average precision (mAP) score, which is considered as a predefined IoU threshold. To generate scene graphs, we introduced the technique of Scene Graph Generation (_SGG_) and selected a state-of-the-art unbiased scene graph generation (_USGG_) framework [22] that used causal inference. Scene graph generation is a task in computer vision that involves creating a structured representation of a visual scene, capturing the relationships between objects and their attributes. The scene graph represents the objects in the scene as nodes  
Figure 4: The pre-trained Faster R-CNN detected objects in an image of a traffic scene from the CCTSDB dataset. Figure 3: The design of data requirement analysis supporting tool. and each directed edge represents the corresponding predicate between the two objects, each scene graph can also be formulated as a set of visual relation triplets (i.e., <sub, pred, obj>), providing a rich and structured understanding of the visual content. Existing scene graph generation is often biased, mainly due to the severe long-tail distribution issue of the dataset, e.g., collapsing diverse humans walk on/sit on/lay on the beach into humans on the beach.