The DOING project aims at developing methods and tools to first extract information from textual data by structuring them in a graph database, and then to manipulate this knowledge graph in an intelligent way. The chosen application domain is the health domain, with first of all the use of freely available data (such as clinical cases). DOING aims at designing data science queries, i.e., a new form of declarative queries, which can integrate analyses, that will guide healthcare specialists in their decision making. DOING is based on a real interdisciplinary collaboration (Natural Language Processing, Databases and Artificial Intelligence) to transform data into information and knowledge. The goal of the project is to concretize proposals from the DOING working group of the RTR-DIAMS and the DOING action of the GDR-MADICS.

The objective of the DOING project is the proposal and construction of methods, algorithms and tools for the transformation of data into information and then into knowledge. The idea is to bring together the expertise of researchers in Natural Language Processing (NLP), Databases (DB) and Artificial Intelligence (AI) to:
1) extract information from textual data and represent it to populate graph databases;
2) propose intelligent methods for the manipulation and maintenance of these databases with new forms of queries.

These objectives are broken down into three tasks that are developed in parallel; their relationship is the main thread of the project .
T1 : Task 1 – Extraction of information from textual data
T2 : Task 2 – Data science queries: language and algorithms
T3: Task 3 – Analysis and prediction of physician needs (not financed by the project)