Knowledge Graphs

Storing Knowledge in Graphs

Knowledge graphs are used to describe people, things, ideas and anything else you can imagine. They capture knowledge in a way that both humans and computers can understand.

What are they?

  • For humans it is intuitive to communicate using graphs. There are labels/terms we use for ideas and things that we agree on, then we share new ideas by linking them to these known terms.

    Knowledge graphs are a digital version of this mechanism. They define terms (classes) to describe things, and properties for classes that store data or links to other classes.

    Sometimes graphs are nicely structured with orderly rows and columns of data. Other times they are jagged and incomplete, because that’s all the data you have. This is not a problem, and is naturally how our brains work when we’re trying to understand something new.

    The use of knowledge graphs by computers is quite exciting because they are reliable, flexible, and able to scale to massive volumes as Google, Facebook and others have proven. The use of knowledge graphs is exploding around the world, and they are being used to support software and AI by many organizations.

What are the potential benefits?

Smarter data, open standards, AI fluency and automated reasoning

  • Without question, the best way to capture metadata (data about data)
  • Based on widely used open standards like RDF, OWL and JSON-LD
  • Provides a common vocabulary for humans and computers
  • Designed to support efficient and flexible automated reasoning

(image credit – Wikipedia)

How can I use them?

There are 2 ways you can use any graph/vocabulary (custom or public)

  1. Reference them
  2. Automate with them

Referencing them is free. It just means you are aware of them, and that data fields you use can be mapped to them where there is overlap. For example, you can fill a drop-down list in forms with codes you copy from a public graph.

Automating with them is even better. It makes you digitally compatible with other members of your community/network, which itself can lead to new opportunities. And it offers quality and productivity benefits similar to smart content.

Tag’s support for knowledge graphs is still under development. More specifically, there is a knowledge graph editor that will soon become a general use Tag app.

You can gain early access to some knowledge graph features using our onboarding services.

Public knowledge graphs

The Learning Center includes a list of Public Resources which list several well-known public knowledge graphs. These graphs contain information that drive business process (e.g., health billing codes, financial codes, other professional industry standards) and improve global communication.

Tag can integrate parts of these vocabularies into smart content templates and forms. For example, instead of generating *.docx documents, you can generate XML fragments described by public graphs (e.g., FHIR resources). In other words, using industry knowledge graphs, Tag can support computer-to-computer communication, and facilitate computer-to-human communication through human-readable fragment generation.

Custom knowledge graphs

Tag can also read custom knowledge graphs created in other tools (e.g., Protege).

We can also be helpful when building custom graphs, which are sometimes created by converting other documents or models. For example, a CSV or other file which contains names of things you want to work with can be used as a quick start when creating custom graphs. This functionality can be turned on during onboarding.

How do I learn more?

Contact us for more information about any of the above features.