Ontology-based NLP information extraction to enrich nanomaterial environmental exposure database

Ali Ayadi (1) , Melanie Auffan (1, 2) , Jérôme Rose (1, 2)

1 CEREGE - Centre européen de recherche et d'enseignement des géosciences de l'environnement
2 Duke University [Durham]

In recent years, nanotechnologies have led to undeniable progress in any domains, such as electronics, materials and medicine. Despite the benefits of such a technology, a careful assessment of the potential risks for Human and Environmental health have to be studied. Assessing exposure and hazard to nanomaterials is a major challenge in the field of environmental sciences. This task requires to gather a large amount of meaningful experimental data usually generated by laboratory experiments. A first database of environmental exposure to nanomaterials (EXPOSED database) has been developed to gather data generated during mesocosm experiments. The challenge is now to enrich this database with more data from scientific articles in related fields. Herein, we present an ontology-based Natural Language Processing (NLP) approach to automatically extract and transfer data from text sources to database. This approach combines the use of NLP techniques and a domain ontology to automatically extract environmental exposure and hazards information. This approach was tested to enrich the EXPOSED database and indicators of quality highlight that this approach is effective and promising.

Mots clés: Database enrichment; Information extraction; Database enrichment; NLP techniques; Domain ontology; Engineered nanomaterials; Mesocosms

Domaines: Sciences de l'environnement

Ali Ayadi, Melanie Auffan, Jérôme Rose. Ontology-based NLP information extraction to enrich nanomaterial environmental exposure database. Procedia Computer Science, 2020, 176, pp.360-369. ⟨10.1016/j.procs.2020.08.037⟩. ⟨hal-03043080⟩