• La tâche s'inscrit dans le scénario "blé tendre" du projet ANR D2KAB.
  • Les données extraites de documents et les données d'observations sur les phénotypes du blé sont gérées/annotées par des concepts de deux ontologies distinctes (WTO et CO_321).


This thesis addresses the extraction of relational information from scientific documents in Life Sciences, i.e. transforming unstructured text into machine-readable structured information. The extraction of semantic relationships between entities detected in text makes explicit and formalizes the underlying structures. Current state-of-the art methods rely on supervised machine learning. Supervised learning, and even more so recent deep learning methods, require many training examples that are costly to produce, all the more in specific domains such as Life Sciences.