Insights

30+ Ways to Use ChatGPT with Graph Technology

Everything’s changing (again). Let’s keep a running list of all the ways you can use ChatGPT, GPT-3, the just-released GPT-4, and related LLMs for knowledge graphs, graph databases, and other graph platforms cases and solutions.

I’ll start with my blog post from a couple of weeks ago, 4 Ways to Use ChatGPT for Graph Databases

  1. Convert text-based, or natural language queries into graph database queries, which we are literally doing in our Gemini Explore product [click here to schedule a call to see it in action]
  2. Data enrichment for more human-readable representation of relationships leading to improved decision-making
  3. Recommendation engines for e-commerce, media, and other applications
  4. Generate synthetic graph data so proprietary or sensitive information isn’t accessed (including sample data)

From Kurt Cagle‘s list (with examples), ChatGPT Tricks for Knowledge Graph Workers

  1. Generating scientific (known) data as an ordered table (periodic table returned as tabular data)
  2. Generating scientific (known) data to a given schema (periodic table returned in Turtle RDF format)
  3. Converting a plain text report into a complex schema (a written police report returned in RDF format)
  4. Generating information graphics from plain text (generate a graph/network diagram from that same report)
  5. Getting supplemental information (DC comics character information returned in specific machine-readable formats)
  6. Entity extraction and content enrichment (extraction from Presidential executive order record into RDF format)
  7. Taxonomy construction (fed an outline, it returns it in RDF format)
  8. Rudimentary reasoning (show all but panthers in a taxonomy list)

Kingsley Uyi Idehen over on Medium has a whole batch of suggestions and amazing (and strangely serene!) videos on YouTube:

  1. Medium: Generate a course description outline from plain text to RDF format
  2. YouTube: Turn a job description into machine-readable format

Mai Văn Khánh on Medium creates diagrams and workflow graphics related to the scenario of “a small software development company wants a tool will allow clients to create projects, assign tasks to team members, and track the progress of the project.” Using the Mermaid.js: (all with in-depth examples)

  1. “Use case diagrams to capture the high-level interactions between the client and the project management tool.
  2. “Class diagrams to model the classes of objects in the system, such as projects, tasks, and team members.
  3. “Sequence diagrams describe the interactions between the client and the system when performing specific tasks, such as creating a new project.
  4. “Activity diagrams to show the flow of activities within the system, such as moving a task from one state to another (e.g., from “in progress” to “completed”). This one is a little iffy.
  5. “Component diagrams that show the physical components of the system and how they relate to each other.
  6. Flowchart diagram showing the order of interactions
  7. State diagram for various objects in the application
  8. Entity relationship diagrams that shows how the various data points are related
  9. A Gantt chart to show the timeline with tasks
  10. Gitgraph showing the “environment developer, staging, and production branching strategy”
  11. User journey diagram (doesn’t really pan out, yet)

And of course our besties at Neo4j are on it with a bonnet:

  1. Konrad Kaliciński details how to turn list of requirements for a graph database into literally the structure with node labels, relationship labels, and property keys
  2. David Stevens walks through generating sample data that their customer success team can use (and reuse) when they can’t get direct access to a prospective customer’s data
  3. Fanghua Yu takes a graph database of movies and actors and turns it into Q&A system

Tomaz Bratanic on Medium:

  1. Create a chatbot with a knowledge graph and ChatGPT

Cobus Greyling from HumanFirst on Medium: (tons of additional writing on LLMs, etc.)

  1. Creating a custom fine-tuned model with GTP-3 language APIs
  2. And a similar process for custom intent classification, in this example, discerning between content about hockey versus baseball

Dean Allemang on Medium:

  1. Using LLMs for narrowing the judgment gap in knowledge graph development and implementation through proposing mappings between an ontology and a data schema

Stay tuned! I’ve got alerts turned on so should be adding to this post pretty frequently!

In the meantime, check out how we’ve integrated OpenAI’s GPT-X APIs with Gemini Explore or start your free trial with Gemini Explore today!