Hi everybody,
Join us next Wed, *Sept 6th, *at 12 PM CT for our monthly webinar. We will
have* Harry Caufield <https://github.com/caufieldjh>,* Lawrence Berkeley
National Laboratory, talking about how *SPIRES* implements Large Language
Models (LLMs) to extract structured knowledge in agricultural science and
beyond.
I have included below more details about the webinar and the Zoom link to
attend the webinar.
I hope you will join us.
Best,
Annarita
:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:
Presenters: Harry Caufield <https://github.com/caufieldjh>, Lawrence
Berkeley National Laboratory
Abstract:
*Staying grounded: assembling structured biological knowledge with help
from large language models*
Developing comprehensive knowledge bases and ontologies demands meticulous
curation. The emergence of highly flexible, artificial intelligence-driven
approaches to natural language processing offers novel ways to expedite
this process. Current methods often rely on extensive training data,
however, and struggle with complex, nested knowledge structures. In this
talk, I will describe a new approach, Structured Prompt Interrogation and
Recursive Extraction of Semantics (SPIRES). This method for information
extraction leverages the capability of Large Language Models (LLMs) to
perform zero-shot learning (ZSL) with a variety of natural-language
prompts. SPIRES operates with predefined data schemas, enabling information
extraction that adheres to these structures. It also grounds concepts with
well-established ontologies and vocabularies, avoiding the "hallucinations"
common to text generated by LLMs.
Within OntoGPT, an LLM-querying framework we have developed, SPIRES
supports rapid application to summarization and modeling across plant
science and biology. Notably, this approach allows customization for new
tasks and topics without a need for new training data. We have found that
OntoGPT and SPIRES are capable of extracting structured knowledge from
large literature collections and constructing knowledge graphs from the
resulting relationships. Through harnessing the language comprehension
capabilities of LLMs, SPIRES streamlines knowledge acquisition from across
agricultural science and beyond.
*| 1P ET | 12P CT | 11A MT | 10A PT |*
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Topic: AgBioData Monthly Webinar
Time: This is a recurring meeting Meet anytime
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Meeting ID: 820 3835 6125
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Hi everybody,
Join us tomorrow, *August 2nd, *at 12 PM CT for a *virtual round-table
discussion on phenotypic data management issues*!
In Year 2 of our current NSF RCN grant, the AgBioData database community
indicated sharing and managing phenotypic data as one of the primary data
issues to address. We have surveyed our member databases, and many of them
agreed that curating phenotypic data is challenging due to *their diversity
in terms of data types* (e.g., images vs. spreadsheets) *and data
sources* (e.g.,
breeding program, experimental trials, or literature), *as well as the lack
of standardization*. We are inviting the AgBioData member databases and the
larger community to discuss these challenges to understand their importance
for AgBioData member databases and if they can be addressed entirely or
partially in a new AgBioData working group.
As usual, the meeting will be 1-hour long and will feature breakout room
sessions! *Zoom link to attend the event at the bottom of this email.*
I hope you will join us.
Best,
Annarita
:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:-:
*| 1P ET | 12P CT | 11A MT | 10A PT |*
Find your local time here
<https://www.timeanddate.com/worldclock/fixedtime.html?msg=Round-Table+discu…>
Topic: AgBioData Monthly Webinar
Time: This is a recurring meeting Meet anytime
Join Zoom Meeting
https://us06web.zoom.us/j/82038356125?pwd=YVFMRElMdEpHZmtObXFvZlA4QVFXQT09
Meeting ID: 820 3835 6125
Passcode: 160683
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---
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• +1 301 715 8592 US (Washington DC)
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• +1 309 205 3325 US
• +1 312 626 6799 US (Chicago)
• +1 646 931 3860 US
• +1 253 205 0468 US
• +1 253 215 8782 US (Tacoma)
• +1 346 248 7799 US (Houston)
• +1 360 209 5623 US
• +1 386 347 5053 US
• +1 507 473 4847 US
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Meeting ID: 820 3835 6125
Passcode: 160683
Find your local number: https://us06web.zoom.us/u/kF3akiFg7