The S-PIC4CHU Project - Semantics-based Provenance, Integrity, and Curation for Consistent, High-quality, Unbiased Data Science.

The effectiveness of data-driven solutions, in Data Science as well as in Machine Learning, clearly depends on the quality and interpretability of the underlying data. Unfortunately, real-world data is often incomplete, inconsistent, biased, or lacks adequate semantic description. Traditional data preparation workflows typically rely on ad hoc methods, limited automation, and minimal consideration of domain knowledge, resulting in inefficiencies and unreliable analytical outcomes. The S-PIC4CHU project proposes embedding semantics at the core of each stage of the process: a novel architecture for data preparation, grounded in a semantics-based methodology that supports provenance tracking, integrity enforcement, and fairness assessment. This paradigm shift is based on the design and implementation of a semantically-aware Data Preparation Pipeline (DPP), integrated with a corresponding Semantic Transformation Pipeline (STP): each data transformation step is semantically annotated through mappings to ontologies and knowledge graphs, enabling enhanced traceability, transparency, and reasoning over data.