SemantiPy: A tool to semantically describe Python code to provide understanding and ensure provenance

This research explores the transformative potential of the semantic web, emphasizing how it can help individuals regain control over their data by making the processes behind data handling more transparent. Unlike traditional data structures, the semantic web connects data meaningfully, representing real-world entities like people and places. This research introduces SemantiPy, a tool developed to automatically describe code semantically in RDF, enabling users to visualize how their data is processed. By leveraging ontologies such as the Function Ontology and the Provenance Ontology, SemantiPy elucidates the flow of data through cleaning pipelines. This enhances transparency regarding how data was manipulated. However, challenges remain, including enhancing accessibility for non-programmers and improving the tool's accuracy. Future enhancements aim to integrate SemantiPy with broader semantic web applications, fostering knowledge sharing and collaboration among developers while promoting transparency in machine learning processes.