Knowledge Graph Creation and Software Testing

Background: With the burgeoning volumes of data, efficient data transformation techniques are crucial. RDF mapping language has been recognized as a conventional method, whileIKEA the Knowledge graph’s approach brings a new perspective with tailored functions and schema definitions. Objectives: This study aims to compare the efficiency and effectiveness of the RDF mapping language (RML) and IKEA Knowledge graph(IKG) approaches in transforming JSON data into RDF format. It explores their performance across different complexity levels to provide insights into their strengths and limitations. Methods: We began our research by studying how professionals in the industry currently transform JSON data into Resource description framework(RDF) formats through a literature review. After gaining this understanding, we conducted practical experiments to compare the RDF mapping language (RML) and IKEA Knowledge graph(IKG)approaches at various complexity levels. We assessed user-friendliness, adaptability, execution time, and overall performance. This combined approach aimed to connect theoretical knowledge with experimental data transformation practices. Results: The results demonstrate the superiority of the IKEA Knowledge graph approach(IKG), particularly in intricate scenarios involving conditional mapping and external graph data lookup. It showcases the IKEA Knowledge Graph (IKG) method’s versatility and efficiency in managing diverse data transformation tasks. Conclusions: Through practical experimentation and thorough analysis, this study concludes that the IKEA Knowledge graph approach demonstrates superior performance in handling complex data transformations compared to the RDF mapping language (RML) approach. This research provides valuable insights for choosing an optimal data trans-formation approach based on the specific task complexities and requirement