STaR: Space and Time-aware Statistic Query Answering

Oana Balalau, Simon Ebel, Helena Galhardas, Théo Galizzi, Ioana Manolescu

Abstract: High-quality data is essential for informed public debate. High-quality statistical data sources provide valuable reference information for verifying claims. To assist journalists and fact-checkers, user queries about specific claims should be automatically answered using statistical tables. However, the large number and variety of these sources make this task challenging.We propose to demonstrate STaR, a novel method for Space and Time-aware STatistic Retrieval, based on a user natural language query. STaR is deployed within our system StatCheck, which we developed and shared with fact-checking journalists. STaR improves the quality of statistic fact retrieval by treating space and time separately from the other parts of the statistics dataset. Specifically, we use them as dimensions of the data (and the query), and focus the linguistic part of our dataset search on the rich, varied language present in the data. Our demonstration uses statistic datasets from France, Europe, and a few beyond, allowing users to query and explore along space and time dimensions.