# Dataset Construction through Ontology-Based Data Requirements Analysis
###### Abstract  
Machine learning (ML) technology is rapidly evolving, and the quality of ML systems is becoming an increasingly focal point of attention. Since the ML system is shaped by the dataset it learns from, its quality largely depends on the quality of the dataset. However, the dataset is often collected in a non-standardized process and few requirements and analysis methods are given to assist in identifying the needed dataset. This leads to no guarantee for the quality of dataset, affecting the generalization ability of model and resulting in low training efficiency. To address these issues, this paper proposes an ontology-based requirement analysis method where ontology integrates domain knowledge into the process of data requirements analysis and the coverage criteria on ontology are given for specifying data requirements which can later be used to guide the high-quality construction of the dataset. We held an experiment on an image recognition system in the field of autonomous driving to validate our approach. The result shows that the ML system trained by the dataset constructed through our data requirements analysis method has a better performance. machine learning system; scene graph generation; data requirement analysis; ontology; coverage criteria +
Footnote †: journal: _Sci_  
1
Footnote 1: https://www. collected randomly and there exists a large amount of invalid data and noisy data. To verify the validity of data, it is necessary to conduct experiments to repeat training experiments with different datasets and determine the appropriate dataset that fits in the corresponding ML system. This approach is not only time-consuming but also resource-intensive. Some works have paid attention to the issues mentioned above. Some researchers gave guidance from an abstract level to allow users to assess the quality of datasets like [2; 3], the majority of which focus on non-functional requirements for dataset of ML systems, such as performance, transparency, privacy, and security. Some papers focused on enhancing the defective dataset to offset the defect of the dataset in the process of data collection. However, most of them lacked the requirements analysis methods related to system theory knowledge and the behavior of the system. In fact, the purpose of selecting data is to determine the behavior of the system, so the relationship between data requirements analysis and system behavior should be considered. Additionally, when constructing data requirements related to system functionality, there are challenges such as dealing with unstructured data requirements and expressing semantic information accurately in complex scenarios. Hence, it is necessary to provide domain knowledge-based semantic and structured models for machine learning prediction models to guide users in obtaining comprehensive datasets containing various conceptual instances from real scenarios. To incorporate semantic information and domain knowledge into the process of data requirements analysis, industry and academia referred to the concept of ontology to support data analysis and mining. Ontology is a formalized knowledge representation method. In contrast to semantic models like knowledge graphs, it not only provides rich semantic expression on domain knowledge but also supports constraint rules and logical inference, making the data requirements based on ontology unambiguous. Furthermore, there are different ontology languages available to define the semantic model, which makes it convenient for generating data requirements. Once ontology is introduced, data requirements specification can be integrated into the context of the target ML system to align the process with the expected system's behavior and to facilitate the process of data collection more accurately and effectively. However, most research on data requirements analysis only considered whether the data in the dataset contain concepts in ontology, without including the potential relationships that may exist between concepts in complex scenarios.