Webinar- SEONT, the Socio-Economic Ontology
Channel: Ontologies In Agriculture
Date: 30/07/2020

During this webinar, Gideon Kruseman and Soohno Kim guide us in the conception, development and content of SEONT, the Socio-Economic Ontology built by the CGIAR and partners to annotate agricultural household surveys.
Xingyi Song presents the machine learning tool, based on natural language processing, developed by the University of Sheffield to extract SEONT terms from 100 core socio-economic questions.
Finally, Berta Miro closes the webinar by unfolding a story about annotating CGIAR survey data using SEONT and other ontologies via the machine learning tool developed by the University of Sheffield.

Tools mentionned:
- SEONT: https://github.com/AgriculturalSemantics/SEONT?mc_cid=df978c232f&mc_eid=4c55a33775
- 100Q: https://bigdata.cgiar.org/blog-post/100q-boosting-household-survey-data-usability-with-100-core-questions/?mc_cid=df978c232f&mc_eid=4c55a33775
- OLS EBI: https://www.ebi.ac.uk/ols/index
- RHoMIS: https://www.rhomis.org/
- GARDIAN: https://gardian.bigdata.cgiar.org/

Speakers:
- Gideon Kruseman, foresight and ex-ante research leader at CIMMYT;
- Soohno Kim, senior data manager at IFPRI;
- Xingyi Song, research associate in NLP at the University of Sheffield;
- Berta Miro, post-doctoral fellow at IRRI.

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