More precisely, our objective is to enrich knowledge bases with events, causalities, precedences, histories, negations and beliefs. We want to extract this type of information at scale from structured and unstructured sources, and allow the machine to reason on this information, i.e. to apply logical arguments to arrive at a reasoned conclusion. For this purpose, we want to bring together research on knowledge representation, on reasoning, and on information extraction.
A new way to make black box models interpretableDigital Trust, Data Science & AI — 17/06/2021Recent advances in Artificial Intelligence (AI) have provided us with very powerful predictive models, which can [...]
Do machines understand what they read?Digital Trust, Data Science & AI — 22/10/2020One of the challenges of natural language processing (NLP) is to get the meaning right. Subtle changes in a sentence can [...]
Four positions opened to work on extracting complex information from natural language textData Science & AI — 02/10/2020We are looking for two PhD, one Postdoc, and one Research Engineer.