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Thursday, January 28 • 01:00 - 01:30
Deep Linking: Machine learning to connect up the PIDs

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Deep Linking: Machine learning to connect up the PIDs

Science is progressive, and every discovery, set of data, and publication builds on previous work. Today, it's impossible to put every new development in the context of what's gone before, especially if research outputs are largely invisible living all over the web disconnected to each other. Meta aims to remove this barrier to scientific progress with its graph of biomedical research connecting up PIDs across "people, places, things." We apply machine learning to the scientific literature as a way to get retrieve more connections between these essential elements. During this session, we will share the work we've done, lessons we're learning, and open up the remaining time as a group discussion on best practices, pitfalls, areas of opportunity.

Moderators
avatar for Maria Gould

Maria Gould

Product Manager, California Digital Library

Speakers
avatar for Alex Wade

Alex Wade

Technical Program Manager, Chan Zuckerberg Initiative
AI

Ana-Maria Istrate

Chan Zuckerberg Initiative
JL

Jennifer Lin

Product Dir of Meta, Chan Zuckerberg Initiative


Thursday January 28, 2021 01:00 - 01:30 UTC
Stage 1