# Dataset Construction through Ontology-Based Data Requirements Analysis
###### Abstract
To address the shortcomings of existing studies, we propose a data requirements analysis method based on an ontology that provides coverage criteria for guiding the specification of data requirements. These criteria consider the coverage of elements such as entities and relationships in ontology, enabling our method to obtain a training dataset with higher quality compared to previously proposed requirements analysis methods. The method mainly contains two critical techniques: _ontology-based domain model_ and _criteria-based data requirements specification_. The former technique presents an ontology-based domain model that describes the knowledge of concepts and relationships between concepts in the domain of the ML system and provides a formal representation, allowing us to automatically apply and analyze the domain model. The latter guides users to specify the data requirements based on two criteria: _attribute coverage criterion_ and _relation coverage criterion_, both of which can guide users in covering crucial elements to affect learning behavior and derive effective data requirements. The data requirements generated based on our method are formalized and structured, which enables us to analyze it automatically, thereby providing automated services for building datasets. To validate the effectiveness of the proposed method in this paper, we conducted experiments on an image recognition system in traffic scenes. We trained the model using a dataset that satisfies the data requirements identified through our proposed method and separately trained the model using a candidate dataset collected randomly [4]. Subsequently, we compared the performance of the models trained with these two datasets. The results demonstrate that the model trained with the dataset collected through data requirements generated by our proposed method exhibits improved predictive performance compared to the model trained with the candidate dataset collected randomly. The candidate dataset are available at [[https://github.com/csust?zhangjm/CCTSDB2021](https://github.com/csust?zhangjm/CCTSDB2021)]([https://github.com/csust?zhangjm/CCTSDB2021](https://github.com/csust?zhangjm/CCTSDB2021)) (accessed on 24 September 2023). It should be noted that our method does not discuss how to construct a domain model completely or use ontology to describe the complex logic of ML systems. The more complete the given domain model is, the more effective the method proposed will be. The focus of our discussion is how to utilize the semantic information through the given domain model effectively and assist users in generating effective data requirements. The more comprehensive and accurate the domain model is, the more potent the impact of our proposed method will be in guiding the generation of data requirements for ML systems. The remainder of this article is organized as follows: Section 2 discusses the related work. Section 3 describes a data requirements analysis method and takes the traffic scene as an example. Section 4 details the design of the experiment and the experimental results. Section 5 summarizes the whole paper.