# Dataset Construction through Ontology-Based Data Requirements Analysis
## 2 Related Work  
ML systems require a significant investment to gather sufficient data encompassing various conditions. Therefore, the majority of research teams utilize open datasets for their experiments. For example, HOTPOTQA [5], BDD100k [6], COCO [7], etc., have been successful open datasets in the field of NLP, image processing and autonomous driving for a long time. While public datasets provide researchers with the convenience of accessing data, it is essential to address their limitations in practical and engineering applications. Most open datasets aim to provide well-synchronized, denoised, and ready-to-use data but are reckless in publishing the details of their hardware configurations and open-sourcing the developing tools, which causes problems for other researchers to create the dataset they need. These researchers tend to customize the collection of datasets based on their own conditions and needs with the risk of low-quality data in the dataset. Thus, some studies focused on improving the quality of datasets in ML systems through the process of collecting. [PERSON] et al. [8] built a tool capable of detecting, interpreting, and remediating problems in data and automatically capturing all changes applied to the data, which can reduce turnaround time in the data preparation pipeline and simplify the data quality assessment process. Some researchers aimed to preprocess the dataset after it had been collected. [PERSON] et al. [9] adopted a multi-strategy filtering and text augmentation algorithm based on the pre-trained model to increase the size and quality of the dataset. Refs. [10; 11; 12] performed enhancement operations such as translation, rotation, noise addition, etc., on the collected data to increase the robustness of the dataset. [PERSON] et al. [13] formulated noisy textual queries removing and noisy images filtering as a multi-view and multi-instance learning problem separately to collect diverse and accurate images for given queries from the Web. Data requirement analysis is an effective way to ensure the quality of the dataset as well. Refs. [14; 15] attempted to analyze data from the perspectives of balance and fairness. [PERSON] et al. [16] presented a data-driven engineering process as a new systematic and structured requirement analysis approach for leveraging the future applications of ML in the industry. Some studies have provided some tools to supply the data requirement analysis, such as [17] which defined a model-driven engineering (MDE) method using the UML semi-formal modeling language for the analysis of dataset structural requirements. However, the above papers did not systematically model the semantics of data in complicated scenarios, failing to address the lack of depth and breadth of data. To address these issues, knowledge engineering is increasingly being applied in the construction process of knowledge graphs and graph data. It can provide original structured knowledge data for intelligent systems and extract semantic information to enhance the datasets used for training. Such systems not only obtain domain knowledge from public data sources (e.g., Wikipedia, Freebase, etc.) but also integrate and link data from the internet (e.g., Semantic Web, Linked Data, etc.). Up to now, research on aspects such as data cleaning [18] and data profiling [19] have mainly focused on relational data,making it difficult to be directly applied to knowledge graphs. This is caused by the issue that, in contrast to relational data, knowledge graphs can be applied without the need for strict tabular structures or predefined schemas and do not need to be strictly normalized. Therefore, the first step to ensure the quality of data is to extract the schema from knowledge graphs and check for consistency. Ontology is a formal method of representing knowledge.