This paper presents an architecture of ontological components for the Semantic Web. Many methods and methodologies can be found in the literature. Generally, they are dedicated to ...
Nesrine Ben Mustapha, Marie-Aude Aufaure, Hajer Ba...
We propose to combine two approaches for modeling data admitting sparse representations: on the one hand, dictionary learning has proven effective for various signal processing ta...
One of the main problems in probabilistic grammatical inference consists in inferring a stochastic language, i.e. a probability distribution, in some class of probabilistic models...
We use Wikipedia articles to semantically inform the generation of query models. To this end, we apply supervised machine learning to automatically link queries to Wikipedia artic...
Probabilistic models of languages are fundamental to understand and learn the profile of the subjacent code in order to estimate its entropy, enabling the verification and predicti...