Résumé
This paper presents a generic semantic model to describe, structure, and integrate the named entities automatically extracted from scientific texts, represented as annotations. This model has been used to construct knowledge graphs from two distinct agricultural corpora consisting of PubMed scientific publications on wheat and rice genetics. The named entities to be recognized are genes, phenotypes, traits, genetic markers, and taxa. For both corpora, named entities were automatically extracted using natural language processing tools. The RDF model was populated using a mapping-based transformation pipeline implemented with the Morph-xR2RML tool which takes CSV files as input. The resulting RDF knowledge graphs are deployed and query-able through dedicated web applications.