List of Publications Related to the S-PIC4CHU Project
Each publication is categorized based on its associated Work Project (WP).
WP1 Models and Architectures for DPPs and STPs
1.1. Semantic architecture and quality measures
Criscuolo, C., Salnitri, M., & Martinenghi, D. (2025). FAIR-CARE: A comparative evaluation of unfairness mitigation approaches. Information and Software Technology, 107898.
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Quintarelli, E., Schreiber, F. A., Stefanidis, K., Tanca, L., & Oliboni, B. (2025). A Conceptual Model for Context Awareness in Ethical Data Management. arXiv preprint arXiv:2511.21942.
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Pano, A., Lanti, D., & Calvanese, D. (2025, October). Virtual Knowledge Graphs over Earth Observation Data. In International Semantic Web Conference (pp. 149-166). Cham: Springer Nature Switzerland.
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1.2. Building blocks for data preparation
Chapman, A., Lauro, L., Missier, P., & Torlone, R. (2024). Supporting better insights of data science pipelines with fine-grained provenance. ACM Transactions on Database Systems, 49(2), 1-42.
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Lazzaro, P. L., Lazzaro, M., Missier, P., & Torlone, R. (2025). PROLIT: Supporting the Transparency of Data Preparation Pipelines through Narratives over Data Provenance. In EDBT (pp. 1138-1141).
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Karkee, S., Botoeva, E., Coombes, S., Jordanous, A., Kafali, Ö., & Lanti, D. (2025). Accessing Semi-structured Data with RML and LLMs.
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1.3. System architecture design
WP2 Improving the Quality of Data
2.1. Managing data inconsistency and incompleteness
2.2. Managing biased data
Tahmasebi, A. (2024). FairDataFlow: a modern solution to ensure data quality and fairness.
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Criscuolo, C., Martinenghi, D., & Piccirillo, G. (2025). A tutorial on intersectionality in fair rankings. arXiv preprint arXiv:2502.05333.
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Ciaccia, P., & Martinenghi, D. (2025). Optimization strategies for parallel computation of skylines. Distributed and Parallel Databases, 43(1), 10.
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Criscuolo, C., Martinenghi, D., & Huang, J. (2025). Reconciling Statistical and Causal Metrics of Fairness in Machine Learning on Data-Driven Systems.
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Ciaccia, P., & Martinenghi, D. (2025). Relevant, yet hard to find: Directional queries to the rescue. In Proceedings of the 33nd Symposium on Advanced Database Systems.
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Balzotti, L., Firmani, D., Mathew, J. G., Torlone, R., & Amer-Yahia, S. (2025, July). R-Fairness: Assessing Fairness of Ranking in Subjective Data. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 32187-32199).
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2.3. Data reduction
WP3 Semantic Enrichment, Provenance, and Explanation
3.1. Semantic enrichment
Xiao, G., Ren, L., Qi, G., Xue, H., Di Panfilo, M., & Lanti, D. (2025, August). LLM4VKG: Leveraging large language models for virtual knowledge graph construction. In Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI).
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Baura, D., & Calvanese, D. (2025). Real-world Assessment of Policy-Protected OBDA.
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3.2. Management of provenance data
Gregori, L., Lazzaro, P. L., Lazzaro, M., Missier, P., & Torlone, R. (2025). An LLM-guided platform for multi-granular collection and management of data provenance: L. Gregori et al. Journal of Big Data, 12(1), 187.
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3.3. Provenance and explanation in the context of OBDA
WP4 Coordination, Experimentation and Impact Enactment
4.1. Progress Report
Alfano, G., Bartolini, I., Calvanese, D., Ciaccia, P., Greco, S., Lanti, D., ... & Trubitsyna, I. (2025). S-PIC4CHU: Semantics-based Provenance, Integrity, and Curation for Consistent, High-quality, and Unbiased Data Science.
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Alfano, G., Bartolini, I., Calvanese, D., Ciaccia, P., Greco, S., Lanti, D., ... & Trubitsyna, I. (2025). S-PIC4CHU: Semantics-Enriched Techniques for Data Preparation in Data Science. In Proceedings of the 4th Italian Conference on Big Data and Data Science (ITADATA 2025), Turin, Italy, September 9-11, 2025 (pp. 1-8).
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