Paolo Papotti

Bio Notes

Paolo Papotti got his Ph.D. degree from the University of Roma Tre (Italy) in 2007 and is an associate professor in the Data Science department at EURECOM (France) since 2017. Before joining EURECOM, he has been a scientist in the data analytics group at QCRI (Qatar) and an assistant professor at Arizona State University (USA). His research is in the broad areas of scalable data management and NLP, with a focus on data integration and information quality.

News

(Complete list)

• 10/2023 - Best paper award at CIKM 2024 for our work "Data Void Exploits: Tracking & Mitigation Strategies".
• 10/2023 - Keynote on SQL and LLM at the BDA conference.
• 10/2023 - Course on 'Computational Methods to Counter Online Misinformation' at the IA2 Autumn School.
• 9/2024 - Awarded a 3IA Chair from the Interdisciplinary Institute for Artificial Intelligence Cote d Azur.
• 8/2024 - Distinguished reviewer award at VLDB 2024.
• 8/2024 - Research paper 'Data Void Exploits: Tracking & Mitigation Strategies' accepted at CIKM 2024 (pdf).
• 7/2024 - Research paper 'FINCH: Prompt-guided Key-Value Cache Compression for Large Language Models' accepted in TACL (pdf) (code).
• 6/2024 - Research paper 'Generation of Training Examples for Tabular Natural Language Inference' presented at SIGMOD 2024 (pdf) (code).
• 5/2024 - Invited talk on 'SQL and Large Language Models: A Marriage Made in Heaven?' at MIT, UMass, Cornell Univ., NYU, Exeter Univ., Huawei and DBML@ICDE.

Recent Activities

(Complete list)

• Associate Editor: SIGMOD (2025), VLDBJ (since 2023), ICDE (2025)
• PC Member: SIGMOD (2024, 2023), VLDB (2024, 2023), EDBT (2025, 2024), ACL (2023), QDB (2023), SEBD (2024, 2023), BDA (2023), NeurIPS (2024), TaDA@VLDB (2024, 2023), TRL@NeurIPS (2024, 2023)

Selected Publications

Data Cleaning

• R. Cappuzzo, P. Papotti, S. Thirumuruganathan
  Relational Data Imputation with Graph Neural Networks.
  In EDBT, 2024. (.pdf) (code)
• R. Shrestha, O. Habibelahian, A. Termehchy, P. Papotti
  Exploratory Training: When Annotators Learn About Data.
  In SIGMOD, 2023. (.pdf)
• R. Cappuzzo, P. Papotti, S. Thirumuruganathan
  Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks.
  In SIGMOD, 2020. (.pdf) (code) (video)
• S. Ortona, V. Meduri, P. Papotti
  Robust Discovery of Positive and Negative Rules in Knowledge-Bases.
  In ICDE, 2018. (Tech. Report) (code) (.pdf)
• R. Singh, V. Meduri, A. Elmagarmid, S. Madden, P. Papotti, J. Quiane, N. Tang, A. Solar
  Synthesizing Entity Matching Rules by Examples.
  PVLDB, 2016. (.pdf)
• E. Veltri, D. Santoro, G. Mecca, P. Papotti, J. He, G. Li, N. Tang
  Interactive and Deterministic Data Cleaning.
  In SIGMOD, 2016. (.pdf)
• Z. Abedjan, X. Chu, D. Deng, R. Fernandez, I. Ilyas, M. Ouzzani, P. Papotti, M. Stonebraker, N. Tang
  Detecting Data Errors: Where are we and what needs to be done?.
  PVLDB, 2016. (.pdf)
• F. Geerts, G. Mecca, P. Papotti, D. Santoro.
  The LLUNATIC Data-Cleaning Framework.
  PVLDB, 2013. (.pdf) (code)
• X. Chu, I. Ilyas, P. Papotti
  Discovering Denial Constraints.
  PVLDB, 2013. (.pdf)

Computational Fact Checking

• R. Advani et al.
  Maximizing Neutrality in News Ordering.
  (.pdf) KDD, 2023.
• M. Saeed et al.
  Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?.
  (.pdf) CIKM, 2022.
• M. Mori et al.
  Neural machine Translation for Fact-Checking Temporal Claims.
  (.pdf) FEVER, 2022.
• M. Saeed et al.
  RuleBERT: Teaching Soft Rules to Pre-Trained Language Models.
  EMNLP, 2021. (.pdf) (code)
• P. Nakov et al.
  Automated Fact-Checking for Assisting Human Fact-Checkers.
  IJCAI, 2021. (.pdf)
• G. Karagiannis, M. Saeed, P. Papotti, I. Trummer.
  Scrutinizer: a mixed-initiative approach to large-scale, data-driven claim verification.
  PVLDB, 2020. (.pdf) (code) (video)
• P. Huynh, P. Papotti.
  A Benchmark for Fact Checking Algorithms Built on Knowledge Bases.
  CIKM, 2019. (.pdf) (code)
• N. Ahmadi, J. Lee, P. Papotti, M. Saeed.
  Explainable Fact Checking with Probabilistic Answer Set Programming.
  Conference for Truth and Trust Online (TTO), 2019. (.pdf) (code)

Table Representation Learning

• Saeed, De Cao, Papotti
  Querying Large Language Models with SQL.
  In EDBT (Vision), 2024. (code) (pdf) (blog post)
• Papicchio, Papotti, Cagliero
  QATCH: Benchmarking SQL-centric tasks with Table Representation Learning Models on Your Data.
  In NeurIPS (Dataset and benchmark track), 2023. (code) (pdf)
• M. Hulsebos, X. Deng, H. Sun, P. Papotti
  Models and Practice of Neural Table Representations.
  In SIGMOD (Tutorial), 2023. (code) (slides) (video)
• G. Badaro, M. Saeed, P. Papotti
  Transformers for Tabular Data Representation: A Survey of Models and Applications.
  In Transactions of the ACL (TACL), 2023. (.pdf)
• M. Saeed, P. Papotti
  You are my type! Type embeddings for pre-trained language models.
  In EMNLP (Findings), 2022. (.pdf) (code)
• E. Veltri, G. Badaro, M. Saeed, P. Papotti
  Data Ambiguity Profiling for the Generation of Training Examples.
  In ICDE, 2023. (.pdf) (code)
• G. Badaro, P. Papotti.
  Transformers for Tabular Data Representation: Models and Applications.
  VLDB (Tutorial), 2022. (.pdf) (slides)
• E. Veltri, D. Santoro, G. Badaro, M. Saeed, P. Papotti
  Pythia: Unsupervised Generation of Ambiguous Textual Claims from Relational Data.
  In SIGMOD (demo), 2022. (.pdf) (code)
• N. Ahmadi, A. Sand, P. Papotti.
  Unsupervised Matching of Data and Text.
  ICDE, 2022. (.pdf) (code)

Data Exchange

• P. Atzeni, L. Bellomarini, P. Papotti, R. Torlone.
  Meta-Mappings for Schema Mapping Reuse.
  PVLDB, 2019. (.pdf)
• B. Marnette, G. Mecca, P. Papotti.
  Scalable Data Exchange with Functional Dependencies.
  PVLDB, 2010. (.pdf) (.ppt) (code)
• G. Mecca, P. Papotti, S. Raunich.
  Core Schema Mappings.
  In SIGMOD Conference, 2009. (.pdf) (.ppt) (tech. report) (code)
• M.A. Hernandez, P. Papotti, W.C. Tan.
  Data Exchange with Data-Metadata Translations.
  In VLDB Conference, 2008. (.pdf) (.ppt)
• A. Raffio, D. Braga, S.Ceri, P. Papotti, M.A. Hernandez.
  Clip: a Visual Language for Explicit Schema Mappings.
  In ICDE Conference, 2008. (.pdf)
• A. Fuxman, M.A.Hernandez, H.Ho, R.J. Miller, P. Papotti, L.Popa.
  Nested Mappings: Schema Mapping Reloaded.
  In VLDB Conference, 2006. (.pdf) (.ppt)

Web Data Extraction and Integration

• M. Bronzi, V. Crescenzi, P. Merialdo, P. Papotti.
  Extraction and Integration of Partially Overlapping Web Sources.
  PVLDB, 2013. (.pdf)
• L.Blanco, V.Crescenzi, P.Merialdo, P.Papotti.
  Probabilistic Models to Reconcile Complex Data from Inaccurate Data Sources.
  In CAiSE Conference, 2010. (.pdf)

Schema Exchange

• P. Papotti and R. Torlone.
  Schema exchange: Generic mappings for transforming data and metadata.
  In Data & Knowledge Engineering, 2009. (.pdf)
• P. Papotti and R. Torlone.
  Automatic Generation of Model Translations.
  In CAiSE Conference, 2007. (.pdf)