Knowledge Graph
A network of entities and the relationships between them, queryable like a map.
/ quick answer
A Knowledge Graph stores information as nodes (entities) and edges (relationships) instead of rows. It lets you answer 'how is X connected to Y?' in one hop instead of stitching joins. A network of entities and the relationships between them, queryable like a map.
/ frequently asked
What is Knowledge Graph?
A Knowledge Graph stores information as nodes (entities) and edges (relationships) instead of rows. It lets you answer 'how is X connected to Y?' in one hop instead of stitching joins.
What is an example of Knowledge Graph?
Onexial itself is a knowledge graph: each node is a concept, workflow, tool, prompt or use case, automatically linked to the others it references.
Why does Knowledge Graph matter?
A network of entities and the relationships between them, queryable like a map.
What should I learn after Knowledge Graph?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Data Residency Compliance
Data Residency Compliance refers to the legal requirement for data, particularly personal or sensitive data, to be stored and processed within specific geographic boundaries, typically a country or region.
- →Knowledge Distillation
Training a small model to imitate the outputs of a large one, keeping most of the quality on a target task at a fraction of the cost.
Related workflows
Turn this into a repeatable process.
- →Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
- →Data Residency Audit Workflow
This workflow details the systematic steps for auditing an organization's data storage and processing locations to verify compliance with various data residency regulations.
- →Dia: Automate Customer Support Research
This workflow utilizes Dia, an AI browser, to automate information gathering for complex customer support inquiries by searching across internal knowledge bases and external documentation. It reduces agent resolution time and improves consistency in responses.
- →Personal Research Assistant Workflow
A repeatable system to research any topic deeply in under 30 minutes.
Related tool stacks
The tools that run it in production.
- →Knowledge Graph Stack
Relationship layer that maps concepts, workflows, prompts, tools, and cases.
- →RAG Context Enrichment Stack
A technical stack designed to enrich the contextual data provided to a Retrieval Augmented Generation (RAG) system, improving the quality and depth of LLM responses.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
- →RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
Related prompts
Reusable prompts for this job.
- →Competitor Discovery Prompt
Surface and structure direct competitors for a given product.
- →Grounded Answer Prompt
Force the model to answer only from provided sources, with citations.
- →Viral Hook Generator Prompt
Produce 10 scroll-stopping hooks for a topic and platform.
- →Cold Email Sequence Prompt
Draft a 3-touch personalized outbound sequence per lead.
Related use cases
How people apply it, and what came out.
- →Support Team Replaces Wiki Sprawl With a Knowledge Graph
A support org connected policies, playbooks, tickets, and RAG answers into one system.
Comparisons & alternatives
Pick between the options.
- →Vector Database vs Knowledge Graph
Similarity retrieval versus explicit relationship mapping.
- →RAG vs Fine-Tuning
When to retrieve, when to retrain.
- →Zapier vs Make (Integromat)
Which no-code automation platform fits your operation.
- →GPT vs Claude for Business Workflows
Choosing the right model family for production use.