One of today’s major issues in data science is the design of algorithms that allow analysts to effi- ciently infer useful information and knowledge by collaboratively inspecting heterogeneous informa- tion sources, from structured data to unstructured content. Taking data journalism as an emblematic use-case, the goal of the project is to develop the scientific and technological foundations for knowledge- mediated user-in-the-loop collaborative data analytics on heterogenous information sources, and to demonstrate the effectiveness of the approach in realistic, high-visibility use-cases. The project stands at the crossroad of multiple research fields—content analysis, data management, knowledge represen- tation, visualization—that span multiple Inria themes, and counts on a club of major press partners to define usage scenarios, provide data and demonstrate achievements.
|Dates: May 2017 – December 2020
Contacts: Guillaume Gravier & Laurent Amsaleg