SEONT: Semantics & mapping exercise to add structure to messy socio-economic data
This session is the fifth webinar of the series: All about our products and their uses, organised by the Ontologies Community of Practice. 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.
Saved in:
| Format: | Video biblioteca |
|---|---|
| Language: | English |
| Published: |
CGIAR Platform for Big Data in Agriculture
2020
|
| Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Webinar, Socioeconomic Data, Standardization, Big Data, ONTOLOGY, MACHINE LEARNING, DATA, AGRICULTURAL RESEARCH, DATA ANALYSIS, STANDARDIZING, SURVEYS, |
| Online Access: | https://hdl.handle.net/10883/21256 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|