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Knowledge Graphs: The standard for accurate AI

Make your data mean something

A knowledge graph unifies your technical, business, and operational metadata into one flexible, machine-readable model, so AI and people can finally reason over what your enterprise actually knows.

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Knowledge graphs and agents

Large models reason well and know nothing about your business. More models won't fix data your agents cannot interpret. A knowledge graph grounds them in your authoritative context, verified and connected, so answers are accurate and explainable instead of invented. It's why graph-based retrieval outperforms document-only RAG on enterprise questions.

Grounded answers

Connect data to meaning and lineage so agents stop filling gaps with guesses. Fewer hallucinations, answers you can trace.

GraphRAG

Feed retrieval-augmented generation verified, connected context instead of a pile of loosely ranked documents. The graph gives the model the relationships, not just the text.

Agents that can act

Give agents a governed map of the enterprise so they can find gaps, validate entries and take action someone will stand behind.

What is a knowledge graph?

A model that stores data the way you actually see it, as entities tied together by relationships, attributes, constraints and events, so software can reason over it rather than just retrieve it. Everything connected, machine-readable, and built on open standards.

A knowledge graph connects everything

TQ unifies every entity, attribute and relationship across your enterprise into one connected semantic model, turning fragmented, siloed metadata into ground truth your teams and your agents can rely on.

We helped write the standards knowledge graphs run on

20+ years working in semantics. Our team helped author the open W3C standards knowledge graphs are built on, and wrote the field’s standard text along the way.

Open standards

Authored by our team at the W3C

Your models are built on the open standards we helped write: portable by design, no proprietary format, no lock-in.

RDFResource Description FrameworkOWLWeb Ontology LanguageThe canonical text

We wrote the book on it

Semantic Web for the Working Ontologist: the field’s standard textbook, written by our team.

Explore the book →SKOSSimple Knowledge Organization SystemSHACLShapes Constraint Language
1st

US company devoted to the semantic web

300+

prebuilt, reusable ontologies

200+

connectors into your existing stack

Data that carries its own meaning

Knowledge graphs are self-descriptive. They store meaning alongside structure, using shared semantic models called ontologies.

Unified

One shared model your whole team builds on together. No more conflicting definitions.

Flexible

Open standards let you extend, connect and integrate without re-architecting.

Intuitive

Modelled the way you think, so search, discovery and automation all get smarter.

How a knowledge graph is built

Meaning and structure, combined, through ontologies and open standards. Step through how it comes together.

Step 01

Semantic models

Ontologies define your business language: so “customer owns account” means the same thing to every team, tool, and AI.

One graph. Every industry.

The same connected foundation adapts to the hardest data problems in every regulated, multi-system enterprise.

Financial services

Trace customer, product, and regulatory relationships, and prove every audit.

Life sciences

Unify research, trials, specifications, and regulatory documentation.

Manufacturing

Connect product, supply-chain, and quality data to reduce risk.

Media & publishing

Organize content, assets, and workflows for faster discovery.

Public sector

Unify legacy systems across departments and enforce policy with confidence.

See it on your data

Get a walkthrough mapped to your industry and use cases.

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Knowledge graph FAQs

What teams ask when they start connecting data to meaning.

What is a knowledge graph?

A machine-readable model of entities and the relationships between them, with meaning attached, so software can reason over data rather than just retrieve it.

What's the difference between a knowledge graph and a database?

A relational database stores data in tables and drops the relationships between them at query time. A knowledge graph keeps those relationships as first-class, meaningful connections, so context travels with the data.

What's the difference between a knowledge graph and an ontology?

The ontology is the model of meaning: the concepts, relationships and rules. The knowledge graph is that model populated with real data. The ontology is the map. The knowledge graph is the map filled in.

How do knowledge graphs help AI and LLMs?

They ground models in verified, connected context, which reduces hallucination and makes answers explainable and traceable. This is the basis of GraphRAG.

What is GraphRAG?

Retrieval-augmented generation that retrieves from a knowledge graph alongside or instead of a document store, so the model gets the relationships between facts rather than isolated passages.

What standards are knowledge graphs built on?

Open W3C standards: RDF and RDFS for data, OWL for ontologies, SPARQL for querying, SHACL for validation, SKOS for taxonomies. Our team helped author several of them.