[Esip-agclimate] ACC telecon on Tuesday 2021-04-06 @ 1500 hrs eastern on Knowledge Graphs for Environmental Risk Management: Technical Overview of Neo4j Technologies for Semantics / AI / ML

[Brian Wee]

Hi,

At our next scheduled call of the Agriculture and Climate Cluster (ACC), we
have arranged for a talk titled "Knowledge Graphs for Environmental Risk
Management: Technical Overview of Neo4j Technologies for Semantics / AI /
ML" presented by Neo4j.

You may also recall earlier demonstrations of using the Neo4j graph
database to visualize the conceptual connections between various documents
as it relates to agriculture and carbon management.  As you've seen from
our previous monthly meetings and the recent 2021 ESIP Winter Meeting
ACC-organized sessions, we're continuing with our push a broad range of
science and technology issues to foster transdisciplinary thinking for
managing agro-ecosystems.  During the ACC call, Bill Teng will also be
highlighting a possible Fall 2021 workshop, if approved by the USDA, that
we'll be designing over the next few months.

Agenda for the ACC call:
https://docs.google.com/document/d/1xCLTjuk84Yf5uBtwGXXII58ugzXdZoDogt7SQkIL6zE/edit#

Zoom URL:
https://us02web.zoom.us/j/82026434814?pwd=Z0lnYUxzZklkUm10VEZKT015b3luZz09

Zoom Passcode: 896682

Full Zoom connection details below, after the abstract.


*Abstract*

Humans interact with the environment constantly, but there are many
environmental risks, such as climate change and natural disasters, air
pollution and chemical safety, that affect the quality of life, years of
healthy life lived, and health disparities. Environmental risks come at a
very high cost. For example, a new study on the comprehensive cost of
wildfires estimates that the 2018 California wildfires cost the state
around $150B and thousands of deaths.

Environmental risks are difficult to monitor and manage because there are
many such risks, especially those related to climate change, water, energy
use, waste and biodiversity. These risks are often interconnected (i.e.,
compound or cascading).

Graph databases are increasingly being used to analyze and act on
connections, whether for risk management and compliance, master data
management, real-time recommendations or analytics and machine learning.
This presentation will cover the following:

   1. Introduction to Neo4j and knowledge graphs
   2. How Neo4j’s underlying data model (the property graph model) relates
   to other graph models, including RDF
   3. Practical demonstration of building a knowledge graph using NLP and
   SWEET ontology in Neo4j


*Attendees may elect to receive a printed copy of the O’Reilly book, Graph
Algorithms: Practical Examples in Apache Spark & Neo4j.*


*Zoom connection details:*

https://us02web.zoom.us/j/82026434814?pwd=Z0lnYUxzZklkUm10VEZKT015b3luZz09
Meeting ID: 820 2643 4814
Passcode: 896682

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--

Brian Wee, Ph.D. <https://www.linkedin.com/in/weebrian>
Founder and Managing Director
massiveconnections.com
+1 (202) 370-7891
Washington, D.C.


Co-chair of the
Earth Science Information Partners <https://www.esipfed.org/> (ESIP)
Agriculture and Climate Cluster
<https://wiki.esipfed.org/Agriculture_and_Climate>
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