The NoRDF Project Modeling and extracting complex information from natural language text

The NoRDF Project is a scientific project at Télécom Paris that aims to model and extract complex information from natural language text. More precisely, we want to enrich knowledge bases with events, causation, conditions, precedence, stories, negation, and beliefs. In particular, we will investigate the expression of sentiment.

We want to extract this type of information at scale from structured and unstructured sources, and we want to allow machines to reason on it. The project brings together research on knowledge representation, on reasoning, and on information extraction, and aims to be useful for applications such as fake news detection, the modeling of controversies, or the analysis of the e-reputation of a company.

Selected and financially supported by the French National Research Agency (ANR) and the French Defense Innovation Agency (AID) as part of the National Program for AI, the project has a total budget of 1.3 million euros for a four-year period. It also draws on the experience, use cases and financial support of four industrial partners: Converteo, EDF, Groupe BPCE and Schlumberger.

The NoRDF project is led by Fabian Suchanek, Professor at Télécom Paris, Institut Polytechnique de Paris, specialist in knowledge bases and ontologies and co-creator of the famous YAGO knowledge base. Chloé Clavel, Professor at Télécom Paris and specialist in natural language processing, is the co-leader of the project on the sentiment analysis component.

See a slideshow
Watch a talk