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Webinar – Can machine learning technologies be useful to create or complete ontologies in agriculture?

CGIAR Webinar by the Platform for Big Data in Agriculture’s Community of Practice on Ontologies – Can machine learning technologies be useful to create or complete ontologies in agriculture?

Speakers
Milko Škofič

Milko Škofič
Expert in information systems design, data analysis and capacity building

Milko Škofič  is a systems analyst with over 20 years of experience in design, development and implementation of plant genetic resources and forestry information systems. He has extensive experience in collecting, aggregating and analyzing data from heterogeneous and geographically dispersed scientific networks. Currently he supports the creation of national information platforms for nutrition in 10 countries.

 

Diana Maynard

Diana Maynard
Senior research fellow at the University of Sheffield

Dr. Diana Maynard is a senior research fellow at the University of Sheffield, UK, where she has been researching and developing tools for text mining and social-media analysis since 2000. She is one of the key developers of the widely used open source Natural Language Processing (NLP) toolkit GATE, and has special interests in multilinguality, social media, and sentiment analysis.

 

Xingyi Song

Xingyi Song
Research associate at the University of Sheffield

Xingyi Song is a research associate at the Natural Language Processing (NLP) group (GATE Team) managed by Prof. Kalina Bontcheva, working in the biomedical domain name/entity and sentiment recognition and analysis. He holds a PhD from the University of Sheffield in Training Machine translation for human acceptability. Song’s work mainly focuses on machine translation, natural language processing, and machine learning.

 

Read a summary of the webinar.

June 12, 2019

Ontologies Community of Practice


CGIAR Platform for Big Data in Agriculture
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