Paolo Papotti: “Explainable Fact Checking for Statistical and Property Claims”

Paolo Papotti will present his work on fact checking, in our team, on October 15th at 2pm. It will be online on Zoom at

Title: Explainable Fact Checking for Statistical and Property Claims

Abstract: Misinformation is an important problem but fact checkers are overwhelmed by the amount of false content that is produced online every day. To support fact checkers in their efforts, we are creating data driven verification methods that use structured datasets to assess claims and explain their decisions. For statistical claims, we translate text claims into SQL queries on relational databases. We exploit text classifiers to propose validation queries to the users and rely on tentative execution of query candidates to narrow down the set of alternatives. The verification process is controlled by a cost-based optimizer that considers expected verification overheads and the expected claim utility as training samples. For property claims, we use the rich semantics in knowledge graphs (KGs) to verify claims and produce explanations. As information in a KG is inevitably incomplete, we rely on rule discovery and on text mining to gather the evidence to assess claims. Uncertain rules and facts are turned into logical programs and the checking task is modeled as a probabilistic inference problem. Experiments show that both methods enable the efficient and effective labeling of claims with interpretable explanations, both in simulations and in real world user studies with 50% decrease in verification time. Our algorithms are demonstrated in a fact checking website (, which has been used by more than twelve thousands users to verify claims related to the coronavirus disease (COVID-19) spreads and effects.

Bio: Paolo Papotti is a professor at EURECOM, France since 2017. He got his PhD from Roma Tre University (Italy) in 2007, and had research positions at the Qatar Computing Research Institute (Qatar) and Arizona State University (USA). His research is focused on data integration and information quality. He has authored more than 100 publications, and his work has been recognized with two “Best of the Conference” citations (SIGMOD 2009, VLDB 2016), a best demo award at SIGMOD 2015, and two Google Faculty Research Award (2016, 2020). He is associate editor for PVLDB and the ACM Journal of Data and Information Quality (JDIQ).

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