GraphOntologyrelationship intelligence layer

Synthetic playground

Graph Intelligence Demos

Demos transform example domain text into entity maps, relationship paths, inferred links, and traversal explanations.

The important demonstration is not extraction theater. It is the structured handoff from language into an inspectable graph model.

relationship model

Domain text becomes typed entities and edges.

Traversal paths show explainable relationship chains.

Inferred links are labeled separately from extracted relationships.

operational artifacts

What this layer makes inspectable.

ontology extraction demo

This example artifact shows the graph pattern without exposing proprietary ontology weights, private entity maps, hidden prompts, or internal orchestration logic.

path traversal animation

This example artifact shows the graph pattern without exposing proprietary ontology weights, private entity maps, hidden prompts, or internal orchestration logic.

confidence visualization

This example artifact shows the graph pattern without exposing proprietary ontology weights, private entity maps, hidden prompts, or internal orchestration logic.

semantic playground

Translate example text into graph-native structure.

input

A public transit hub connects a microgrid, emergency services, water sensors, and maintenance teams through shared incident reports.

graph-native ontology model

{
  "domain": "City Infrastructure",
  "provenance": "synthetic/example",
  "extracted": [
    {
      "entity": "Transit Hub",
      "type": "System",
      "attribute": "operational-node",
      "relationship": "connects_to",
      "target": "Microgrid",
      "confidence": 0.74
    },
    {
      "entity": "Microgrid",
      "type": "System",
      "attribute": "context-node",
      "relationship": "reports_to",
      "target": "Emergency Services",
      "confidence": 0.775
    },
    {
      "entity": "Emergency Services",
      "type": "Organization",
      "attribute": "operational-node",
      "relationship": "depends_on",
      "target": "Water Sensors",
      "confidence": 0.81
    },
    {
      "entity": "Water Sensors",
      "type": "System",
      "attribute": "context-node",
      "relationship": "monitors",
      "target": "Maintenance Team",
      "confidence": 0.845
    },
    {
      "entity": "Maintenance Team",
      "type": "Organization",
      "attribute": "operational-node",
      "relationship": "dispatches",
      "target": "Transit Hub",
      "confidence": 0.88
    }
  ]
}

Transit Hub

74%

connects_to

Microgrid

System / operational-node

Microgrid

78%

reports_to

Emergency Services

System / context-node

Emergency Services

81%

depends_on

Water Sensors

Organization / operational-node

Water Sensors

85%

monitors

Maintenance Team

System / context-node

Maintenance Team

88%

dispatches

Transit Hub

Organization / operational-node

developer posture

Patterns for implementation without leaking private systems.

Stable identifiers

Give entities durable IDs before adding inference. A graph that cannot distinguish identity from mention cannot safely reason over relationships.

Typed semantic edges

Use edge labels that are specific enough to constrain traversal. A vague connection is weaker than a typed relationship with provenance.

Inspectable outputs

Show entities, types, attributes, relationships, edge labels, confidence, provenance, and inferred links as separate fields.
Return to graph root

reusable graph explorer

Inspect, adapt, report, and embed ontology graphs from one shared package.

GraphOntology.com is the public shell. Host apps keep ownership of their data, adapters, permissions, and runtime state while the explorer provides a consistent inspection surface.