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The Slack is here to stay!

We are keeping the KGC Conference Slack open permanently, as a central hub for the KG Community to meet and discuss all things kg. The Slack is now open to the public, so feel free to join, introduce yourself and your projects, and invite others:



KGs for Business

Markets today are being reshaped by a new kind of firm--one in which AI runs the show. You'll recognize the names of these high-tech giants--folks like Google, Facebook, Alibaba, and JP Morgan Chase. Turns out they are all known knowledge graph builders.

In fact, nine out of the ten most value-creating companies in the world harness the power of knowledge graphs (the one exception? Berkshire Hathaway). Alan Morrison at PwC describes how Amazon, Facebook, Google and other companies who use standards-based knowledge graphs are able to extend this architecture to develop analytics and operations into the future and leverage other AI technologies. Since the data in a knowledge graph is reusable, there's really no end to its practical applications and, thus, ROI.

As Alan warns, this transition challenges the culture status quo, requiring companies to shift from app-centric to data-centric architecture. It's a big shift, yes--but one companies can't afford not to make as we move deeper into the age of AI. In their new book from HBS Publishing, Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World, Marco Iansiti and Karim R. Lakhani show how knowledge--data, analytics, and AI--enables firms to move beyond constraints on scale, scope, and learning that have restricted business growth for hundreds of years. Firms who reinvent themselves first will reshape competition, alter the structure of our economy, and force laggard companies to rearchitect their operating models.

For first-movers, the opportunity of this moment is both unique and tremendous. Recent poll results from the Enterprise Knowledge Graph Foundation show that the top three barriers for enterprise users to adopt kg technologies are organizational inertia, lack of evidence from peers, and lack of integration with current technology. Those who are move quickly to embrace the coming revolution and solve these technical challenges--without waiting for their competitors to succeed first--will be well-positioned to lead their industries. 


KGs for Covid

Interpreting the Data
“I don’t think I can ever think of a scientific field where we’ve had a doubling time of 14 days,” says Sean Gourley, CEO of Primer AI, referring to the torrent of science literature being published on Covid-19. In early April, PrimerAI launched a knowledge graph to synthesize CV-19 research and news into a daily brief (leveraging state-of-the-art natural language understanding and abstractive summarization tools, as well).

John Bohannon, Primer’s Director of Science, described recently in Nature how emergency medical staff used KG early on in the pandemic, when the details of front-line medical care were changing on a daily basis. Primer's Principal Product Engineer, Amy Heineike, provides further information in a recent podcast interview.

Evaluating Risk
To better track cases and inform decision-makers, the San Diego Supercomputer Center has received an NSF award to organize COVID-19 information into a knowledge network integrating health, pathogen, and environmental data. The researchers will launch, by October, a comprehensive semantic integration platform, connecting up-to-date health, social-economic, and demographic characteristics of populations in different areas, as well as biomedical information such as virus strains and genetic profiles. 

Identifying Potential Treatments
Over at Lawrence Berkeley National Lab, KGC Community member Deepak Unni and team are integrating various CV-19 datasets to leverage machine learning tools for link prediction, with the goal of repurposing existing drugs for treatment. Their work is part of the National Virtual Biotechnology Laboratory (NVBL), a broader initiative across all the National Labs. Good luck, Deepak!

Contact Tracing
As Johanna Kappel points out, contact tracing is a graph problem. In her recently updated article, Natalie Feuerstein lays out current practice, and highlights how graphs enable inference detection--in this case, to find contact places shared by multiple confirmed cases. 

What you can do

  • Recreate and query the AWS COVID-19 knowledge graph 
  • Explore papers, patents, existing treatments and medications
  • Discover multiple linked and linkable datasets 
  • Join the open research dataset challenge
  • Check out this github repo for a KG written in biological expression language 

KGs for Social Good

As Covid-19 has shown us, business-as-usual is no longer sufficient; we must also address environmental and social challenges. Because these topics have many of the same knowledge management requirements as business, knowledge graphs can also play an instrumental role here. 

Take climate change, the most complex and challenging issue of our time. A diverse and growing number of actors are working on this problem, from a number of angles. But who is working on what, and how? What is being learned, and what data is being generated?

To connect all of this scattered, siloed information, our sponsor Pool Party has joined with the Renewable Energy and Energy Efficiency Partnership and the Stockholm Environment Institute to build a climate action knowledge graph. This KG will enable ideas and experience to be shared between actors, and the development of new solutions, technologies and best practices. 

Making headway in the computer industry, Peter Henderson and colleagues at StanfordHAI are doing their part to GreenAI. Food for thought--what else can data scientists do? 

KGs + GPT-3

As the release of GPT-3 takes the Internet by storm, we've rummaged up a great post from Michael Galkin on how knowledge graphs can be leveraged together with NLP. We are interested to see what more can be done with KG + GPT-3!

(If you have an NLP system in production currently, or plan to in the near future, be sure to take this NLP industry survey from Ben Lorica at GradientFlow.)



Project Spotlight

AmpliGraph is the first open source library to democratize graph representation learning, enabling brand-new knowledge discovery from existing graphs. Previously limited to research labs, this capability is now accessible as an open source library, lowering entry barriers and bringing machine learning on graphs to the mainstream.

Community member Christian Miles, who runs the newsletter Source/Target, has put together a fun graph for visualizing songs:




Join the Conversation

Good standards are essential in knowledge representation, and useful for collaboration of all kinds. Over the coming months, KGC will explore how we can help the community establish a shared language, starting with the most important term of all: "knowledge graph." In one (or two) sentences, how would you define a knowledge graph?  Give us your 2 cents over in Slack.


Interview with Paco Nathan

Who is Paco Nathan?

I'm Paco from Derwen. Here are a bunch of links to projects, books, videos, articles, etc. I was one of the early "guinea pigs" for the launch of cloud computing circa 2006, and have been involved with large open source projects such as Apache Spark and Project Jupyter, and now the Ray Project.

What are you working on?
Trying to figure out where the world's heading for AI applications subsequent to the CV-19 pandemic, global economic disruption, etc.

How did you become involved in KGs?
I got involved in 1983 during my grad work at Stanford. More recently I was employed at a firm that handled millions of books, videos, articles, conference talks, online courses, etc., where I led a team organizing metadata about that content.

What makes KGs interesting?
I see lots of future in KG work in an increasingly complex world.  KGs allow for really interesting use cases -- especially now with deep learning becoming ubiquitous and many opportunities to leverage graph embedding.

What's something that others may not know about you?
As a teenager I was really into sandboarding. Later I went to West Point for a couple years, until my lungs gave out -- so I switched from Army to AI. Not so long ago I volunteered on a pyrotechnics team (municipal fireworks shows) in rural Texas. Now I live on an apple orchard in Northern California, and make cider.
Read the Full Interview

News from the Community

  • Dr. Clare Hooper shares some interesting conversations from the recent WebSci20 Personalisation and Community workshop.
  • Bill Bosler is seeking volunteers to develop an oil & gas ontology, linking Operations and Information Technologies standards for safer and more efficient field operations (Interested to join? Hit "reply.")
  • Cambridge Semantics was recognized as a top-5 leader in Forrester's Enterprise Data Fabric wave!
  • TigerGraph's Graphathon offers $18,000 in cash prizes to developers in a competition for useful graph architecture. Deadline for entries is September 6.
  • AstraZeneca, Pirical, Enterprise Knowledge and others are hiring. Check out the #jobs channel in Slack for more details.

News from KGC

  • Over 530 people, from 42 countries, 30+ industries, and 300 companies, joined our conference in May.
  • We are proud of our first career fair, and our partners at LionBase.Videos from Microsoft, Aquicore, Data.World and BrightHive are now ready to watch.
  • Our KGC 2020 panel on data intelligence is now available to the public.

Have something to share?

We welcome contributions! Feel free to share on the #news channel in Slack, and we will mention you as a contributor when we share your item.

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