# Dataset Construction through Ontology-Based Data Requirements Analysis
## 5 Experiments and Analysis
### Experimental Setup  
We conduct a series of experiments on the traffic scene dataset [4] to explore the potential impact of semantic information on predicting behavior and validate the effectiveness of our proposed method. In this section, we first describe the dataset used in our experiments. Then we extract entities and attributes from the images and utilize the relations extraction technique SGG to extract triples. Finally, we perform some check experiments to compare the performance of the system trained with different datasets.  
We focus on detecting traffic signs that have the potential to cause traffic accidents in road driving scenarios. For this purpose, we utilize the CCTSDB2021 ([[https://github.com/csust7](https://github.com/csust7) zhangjm/CCTSDB2021]([https://github.com/csust7](https://github.com/csust7) zhangjm/CCTSDB2021), accessed on 24 September 2023) China Road traffic data list, which consists of 16,365 training pictures and 2000 test pictures. To categorize the dataset, we consider three dimensions: category meanings, weather conditions, and sign  
Figure 6: The structure of XML file of domain model.  
Figure 7: The user interface of data requirements analysis tool.  
sizes. Because of the computational limitations, we selected 2000 pictures as training data and 500 pictures as testing data.  
The dataset used for experimentation is targeted at traffic scenes, and we mainly focus on various traffic signs on the road. Therefore, the characteristics of the dataset generally include the factors of environment and weather which can be reflected based on the light, color and other features in the images at the time of the photo, as well as various entities that may affect the recognition of signs in the image, such as vehicles, pedestrians, trees, and so on. Different entities have different interactions and spatial relationships, which may affect the model's recognition ability. Therefore, we are trying to use existing frameworks to extract these factors.  
In Section 3, only partial information was modeled, which did not fully contain the various semantic information within the traffic scenario. In this part, we attempt to utilize the method realized by [23] to obtain as much semantic information related to the traffic scenario as possible. The paper developed a script that utilized the Stanford CoreNLP parser to parse and tokenize 1,326,488 tweets. The terms in each sentence of the retrieved tweets were annotated as triplets, consisting of a subject, predicate, and object. After filtering and merging redundant information, the tweets were processed to 333 records. Among the subject-object pairs with over 20 predicates, they used the Word2 vec model and the Google News corpus as references to filter the predicates based on the similarity scores. Ultimately, they selected the top 10 predicates with the highest similarity scores, which correspond to the different pairs of subjects and objects. Table 3 shows a few subject-object-predicate pairs to describe traffic scenarios.  
We can extract semantic information related to traffic scenes by using the SGG framework mentioned in Section 4. The SGG framework provides a total of 51 relations and 151 detection entities. In Table 4, we present several relations, entities, and corresponding triples derived from SGG. For the check experiment, we select three entities and four relations as the basis of our analysis. We abstract these four relations into the keyword _position_. Therefore, the data requirements generated using our proposed method mainly include entities extracted by the SGG framework and relations elements between entities.