# Dataset Construction through Ontology-Based Data Requirements Analysis
## 6 Conclusions  
The construction of ML systems involves four main stages: data collection, data preprocessing, model training and model deployment. This paper specifically focused on the first stage and proposed a requirement analysis method tailored to ML systems. Unlike obtaining training datasets solely through non-functional requirements during the data collection stage, this method incorporates ontology and utilizes some of its concepts to semantically model the dataset in the domain model, thereby enhancing the functional requirements. This approach provides users with more specific evaluation metrics for datasets, in addition to abstract measures such as fairness and non-discrimination. Additionally, it offers formal expressions combined with the specific scenarios targeted by the system to automatically generate data requirements. The performance of the YOLOV5-based image recognition system was evaluated on the candidate CCTSDB2021 dataset and the dealed-CCTSDB2021 dataset filtered through our data requirements method. The model trained by the processed dataset exhibited superior performance over the baseline model.  
Firstly we construct a domain model, our study introduces the concepts of entity, relation, attribute, and value based on the concepts of Class and ObjectProperty in ontology, which form a semantic topological graph. Formal expressions are provided to distinguish these four concepts, enabling the model file to be parsed into metadata that need to be referenced for data requirement construction. Next, we propose coverage criteria that cover elements in the domain model. These criteria will derive data requirements through the algorithm we provided, automatically generating corresponding data requirements according to the metadata. Additionally, a supporting tool is developed based on this theory, consisting of three components: the ontology analysis module, the data requirements specification module, and the dataset construction generation module. This tool will assist users in generating data requirement specifications more efficiently and effectively, requiring little additional manual effort.  
In the future, we intend to incorporate automated reasoning mechanisms in ontology to improve the development of domain models and explore more comprehensive coverage criteria. In our example, we solely utilized image data to demonstrate our approach. The inclusion of other data types, such as audio, text, and other multi-modal data, in our approach, will be a key focus of our future research.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work is supported by the school-level project of Shanghai University \"Software Requirements Analysis Method for Underground Railway System\".  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.