Managing the functions of government requires a lot of data.

Whether that data is used to set appropriate prices for soybeans, analyze medical research to fight pandemics, or forecast hurricanes in the Atlantic ocean, many connected resources are involved: not just datasets, articles, and reports, but also people, and public and private organizations.

Because of this criss-crossing of projects and organizations, it’s natural to leverage knowledge graphs to organize them, and to help make that data more discoverable.

In this KGC Monthly Report, we look into some of these government use cases.
Read Paco's Entire Brief


Enabling knowledge discovery

With the goal of increasing personalization for users--like helping searchers find content relevant to life events like "having a baby" or "starting a business"--GOV.UK Data Labs undertook its first graph project, using a knowledge graph to represent user journey data. The team learned that the knowledge graph also:

  • helped internal teams and communities find content more easily
  • increased insights through the development of products, such as a journey visualization app
  • upskilled colleagues in data literacy and drove culture change to make better use of data across GOV.UK

For a write-up of their experiences, as well as best practices for building buy-in, read the rest of the article


Sharing knowledge securely with blockchain

The U.S. Air Force's Small Business Innovation Research is currently undertaking a knowledge-sharing proof of concept with semantic graph database Fluree. Because Fluree is based on blockchain, the database provides a crytographically secure method for sharing documents, both internally and with partners. Read more here.


Intelligent Data Lake

The FDA's Center for Drug Evaluation and Research have adopted Cambridge Semantics' graph-driven platform to provide analytics and integration, as part of their Intelligent Data Lake initiative. This will provide an integrated data perspective to elicit new insights about new drug applications, generic formulations, risk evaluation, translational science, and pharmaceutical quality. Read more here.


The Department of Energy has funded a slew of AI exploratory projects, as part of their Scientific Machine Learning for Modeling and Simulation and Artificial Intelligence and Decision Support for Complex Systems programs.

Although the goal of these programs is to develop foundational research on scientific AI/ML and intelligent supports for complex systems management (think cybersecurity, power grid resilience, and other complex systems requiring real-time decisions), at least one graph-based project has entered the mix: "Graph Neural Network Models of Complex Initial Boundary Value Problems that embed Physical Invariances", led by Sandia National Lab.

This echoes Amit Sheth's contention that knowledge-infused learning enhances deep learning (thanks to Joey Yip for the white paper).


  • A new consortium led by the DOE Artificial Intelligence and Technology Office is exploring AI for emergency response.
  • The Joint Artificial Intelligence Center and the Defense Innovation Unit are leveraging military data to detect cancer.
  • Together, the NSF and US Department of Agriculture are committing more than $140 million to establish five new AI research institutes across the country.


This month, three intergalactic KGC Community members shared musings on semantics in space:

  • Audrey Berquand describes a Design Engineering Assistant, which supports space systems engineers by traversing a knowledge graph containing past missions reports, books and journal publications. The DEA can detect similarities between past and current missions--saving those engineers a ton of manual labor.
  • Kurt Cagle found some leads for an ontology of the universe.
  • Robert Rovetto shared the ontology he developed for orbital debris data.
and are working on issues related to corporate identification, the Defense Advanced Research Projects Agency would like to hear from you! They will be hosting a "proposers' day" on September 25, 2020 for their upcoming Young Faculty Award 2021.


Aaron Bradley was first to tweet when Wikipedia finally removed its HTTP redirect from “knowledge graph” to “ontology"...and gave KG its own proper Wikipedia entry. Edit logs show the entry dates back to June 29 this year. Happy birthday, KG!

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  • Oct 1: Knowledge Espresso with Panos Alexopoulos, giving an author's Q&A on his new book "Semantic Modeling for Data." Register and attend for a chance to win a free book!
  • Oct 8: Knowledge Espresso with Luke Feeney, giving us an Introduction to TerminusDB
  • Oct 13: Office hours are restarting. Our first office hours expert is Mike Grove from Stardog, answering questions on knowledge graphs and data fabrics. Submit your questions! (Want to teach? Let us know). 
  • Oct 15: Knowledge Espresso with Daniel Crowe and Tomás Sabat, giving us an Introduction to Knowledge Graphs with Grakn and Graql
  • Oct 16: Francois Scharffe presents on knowledge graphs at the American Statistical Society
  • Oct 23: Office hours with Paco Nathan: open to any and all KG/ML questions!
  • Oct 22: Knowledge Espresso with Dan McCreary on Graph Embeddings
  • Oct 29: Knowledge Espresso with Jon Herke and students from the Futurist Academy, presenting their portfolios
  • Other events are in the works -- keep up to date with the full KGC calendar
Want to speak at an event, or recommend a topic?
Reply to this email, or get in touch through the SlackTwitter, or LinkedIn.



"The formal naming and definitions for the categories, properties, and relations between concepts within a subject area. Often divided between upper ontology (commonly shared relations and objects) and domain ontology (domain-specific definitions of terms). For example, see OWL (web ontology language) used for representing linked data within a graph. First known usage: “Toward Principles for the Design of Ontologies Used for Knowledge Sharing” by Thomas R. Gruber; International Journal of Human-Computer Studies 43 (5–6): 907–928 (1993)."

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