Research on Data Quality Management and Control Methods of Electromagnetic Engineering Knowledge Graph

Data quality management is crucial for electromagnetic engineering knowledge graphs. This paper explores defining data and quality requirements, and methods for cleaning and evaluating it. Data should cover equipment, materials, regulations, simulations, experiments, relationships, and impacts, meeting criteria like consistency, completeness, accuracy, and uniqueness. Cleaning involves deduplication, format standardization, handling missing values, outlier detection, spell-checking, and consistency checks. Evaluation employs standards like consistency, completeness, accuracy, and timeliness, using statistical tools, database queries, and expert review. Case analysis validates these methods’ effectiveness. Effective data management ensures trustworthy knowledge graphs, bursting electromagnetic engineering.