Mining Biomedical Texts to Generate Semantic Annotations
- Others:
- Knowledge acquisition for aided design through agent interaction (ACACIA) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
- Institut de pharmacologie moléculaire et cellulaire (IPMC) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS) ; COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)
- We thank Remy Bars from Bayer Cropscience, the IPMC7 team working on microarray experiments, especially Kevin Le Brigand for fruitful discussions and PACA region which cofunds this work by regional grant
- INRIA
Description
This report focuses on text mining in the biomedical domain for the generation of semantic annotations based on a formal model which is ontology. We start by exposing the generic methodology for the generation of annotations from texts. Then, we present a state of the art on different knowledge extraction techniques used on biomedical texts. We propose our approach based on Semantic Web Technologies and Natural Language Processing (NLP): it relies on formal ontologies to generate semantic annotations on scientific articles and on other knowledge sources (databases, experiment sheets). This approach can be extended to other do-mains requiring experiments and massive data analyses. Finally, we conclude with a discussion about our work and we present some learnt lessons.
Additional details
- URL
- https://hal.inria.fr/inria-00125266
- URN
- urn:oai:HAL:inria-00125266v3
- Origin repository
- UNICA