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.
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| Format: | Video biblioteca |
|---|---|
| Language: | English |
| Published: |
CGIAR Platform for Big Data in Agriculture
2020
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| 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 |
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dig-cimmyt-10883-212562021-08-24T19:50:02Z SEONT: Semantics & mapping exercise to add structure to messy socio-economic data Webinar- SEONT, the Socio-Economic Ontology AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Webinar Socioeconomic Data Standardization Big Data ONTOLOGY MACHINE LEARNING DATA AGRICULTURAL RESEARCH DATA ANALYSIS STANDARDIZING SURVEYS 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. Céline Aubert Gideon Kruseman Soohno Kim Xingyi Song Berta Miro Elizabeth Arnaud Video also available in YouTube: https://youtu.be/gGqTIN4Cx0Q 1:15:03 2021-02-12T21:41:44Z 2021-02-12T21:41:44Z 2020 Video Accepted Version https://hdl.handle.net/10883/21256 English Open Access Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International § CIMMYT manages Intellectual Assets as International Public Goods. In case you want to make non-exclusive commercial use of this item or you want to adapt it in any manner and use such adaptation, please contact cimmyt-knowledge-center@cgiar.org indicating the code/name of this item and the kind of use you intend; CIMMYT will contact you with the terms and conditions for such use. MP4 France CGIAR Platform for Big Data in Agriculture |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Webinar Socioeconomic Data Standardization Big Data ONTOLOGY MACHINE LEARNING DATA AGRICULTURAL RESEARCH DATA ANALYSIS STANDARDIZING SURVEYS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Webinar Socioeconomic Data Standardization Big Data ONTOLOGY MACHINE LEARNING DATA AGRICULTURAL RESEARCH DATA ANALYSIS STANDARDIZING SURVEYS |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Webinar Socioeconomic Data Standardization Big Data ONTOLOGY MACHINE LEARNING DATA AGRICULTURAL RESEARCH DATA ANALYSIS STANDARDIZING SURVEYS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Webinar Socioeconomic Data Standardization Big Data ONTOLOGY MACHINE LEARNING DATA AGRICULTURAL RESEARCH DATA ANALYSIS STANDARDIZING SURVEYS SEONT: Semantics & mapping exercise to add structure to messy socio-economic data |
| description |
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. |
| format |
Video |
| topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Webinar Socioeconomic Data Standardization Big Data ONTOLOGY MACHINE LEARNING DATA AGRICULTURAL RESEARCH DATA ANALYSIS STANDARDIZING SURVEYS |
| title |
SEONT: Semantics & mapping exercise to add structure to messy socio-economic data |
| title_short |
SEONT: Semantics & mapping exercise to add structure to messy socio-economic data |
| title_full |
SEONT: Semantics & mapping exercise to add structure to messy socio-economic data |
| title_fullStr |
SEONT: Semantics & mapping exercise to add structure to messy socio-economic data |
| title_full_unstemmed |
SEONT: Semantics & mapping exercise to add structure to messy socio-economic data |
| title_sort |
seont: semantics & mapping exercise to add structure to messy socio-economic data |
| publisher |
CGIAR Platform for Big Data in Agriculture |
| publishDate |
2020 |
| url |
https://hdl.handle.net/10883/21256 |
| _version_ |
1756086961684086784 |