Key Concepts

Essential concepts related to TrustGraph including knowledge graphs, the semantic web, ontologies, schemas, pub/sub, and more.

beginner

8 min

What is an Ontology?

Learn what ontologies define, what they cannot fix, and why turning ontology-enhanced knowledge into useful answers at scale requires more than a graph database.

#ontology#ontologies#knowledge graphs

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beginner

8 min

What is Determinism for AI?

How governed knowledge and policy decisions make probabilistic AI agents more predictable, auditable, and safe to act.

#determinism#agents#agent governance

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beginner

6 min

What is OntologyRAG?

How TrustGraph uses an ontology to extract domain-specific knowledge and retrieve connected answers without hand-building a graph query path for every question.

#ontology#ontologies#knowledge graphs

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beginner

6 min

Holons, Context Graphs, and Ontologies

Learn how context graphs provide the three-layer architecture that enables trustworthy, explainable AI through systems of intelligence, records, and temporal feedback.

Why can't I do this with PostgreSQL?

Exploring the fundamental differences between relational databases and TrustGraph's holonic knowledge graph, and why relying on JOINs for complex, context-heavy AI data is an exercise in friction.

#TrustGraph#PostgreSQL#GraphRAG

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beginner

5 min

Zero API Tax: How TrustGraph Makes Token Consumption Irrelevant

Exploring how TrustGraph's self-hosting capabilities eliminate API costs and make token limits a thing of the past by deploying open-weight LLMs you fully control.

#TrustGraph#Self-Hosting#LLMs

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intermediate

9 min

Ontologies and Context Graphs

Ontologies are the semantic grounding infrastructure that makes context graphs legible to AI. Learn how OWL ontologies define meaning, guide knowledge extraction, and enable explainability in TrustGraph.

#ontologies#context-graphs#owl

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beginner

9 min

Context Graph vs. Knowledge Graph

A context graph is a knowledge graph—but one purpose-built for AI. Learn how ontologies, graph storage, and reification of agentic behavior combine to create the semantic grounding infrastructure that makes AI systems reliable.

#context-graphs#context graph#knowledge-graphs

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intermediate

9 min

How Specialized Context Improves AI Reliability

Structured, domain-specific context delivered through a context graph reduces LLM hallucinations, improves answer precision, and enables AI systems to cite sources. Here is the mechanism and the evidence.

#context graph#context graphs#ai-reliability

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beginner

7 min

What is a Context Backend?

A context backend manages, stores, and retrieves the structured knowledge that AI agents use to answer questions reliably. Learn how TrustGraph compares to Supabase as a context backend—and why zero third-party dependencies changes the equation.

#context graph#context graphs#context-backend

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Advanced

7 min

Graph Reification

Learn how graph reification enables AI agents to track provenance, manage temporal relationships, and build sophisticated memory systems through RDF 1.2 and quoted triples.

#RDF#Knowledge Graphs#Reification

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intermediate

10 min

Understanding Context Graphs

Learn how context graphs provide the three-layer architecture that enables trustworthy, explainable AI through systems of intelligence, records, and temporal feedback.

#context-graphs#knowledge-graphs#context-engineering

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intermediate

9 min

Agent Memory

Explore how Knowledge Graphs enable persistent, structured memory for AI agents. Learn about short-term, long-term, and episodic memory patterns for intelligent agents.

#agent-memory#ai-agents#knowledge-graphs

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intermediate

7 min

Context Engineering

Learn how Context Engineering shapes AI responses by carefully selecting and structuring information from Knowledge Graphs. Master the art of building precise, relevant context for LLM queries.

#context-engineering#llm#knowledge-graphs

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intermediate

9 min

GraphRAG: Graph-Based Retrieval-Augmented Generation

Discover how GraphRAG combines Knowledge Graphs with vector search to enable multi-hop reasoning and relationship-aware context for LLMs. Learn the advantages over traditional RAG.

#graphrag#rag#knowledge-graphs

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intermediate

9 min

Interoperability

Learn how Knowledge Graphs enable seamless data exchange across systems through standard formats, protocols, and vocabularies. Master interoperability patterns for connected AI systems.

#interoperability#integration#standards

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intermediate

8 min

Knowledge Cores as Modular Memory

Understand how Knowledge Cores provide modular, swappable Knowledge Graph storage for AI agents. Learn to organize, version, and manage multiple knowledge bases.

#knowledge-cores#modular-memory#knowledge-graphs

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advanced

13 min

Ontology RAG: Schema-Driven Knowledge Extraction

Learn how Ontology RAG uses formal ontologies to extract structured, typed knowledge from unstructured text. Understand when to use schema-driven extraction vs. schema-free GraphRAG.

#ontology-rag#rag#ontology

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intermediate

9 min

Semantic Structures

Learn how semantic structures organize knowledge with meaning and context. Explore ontologies, schemas, taxonomies, and how they enable machine-understandable knowledge representation.

#semantic-structures#ontology#schema

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intermediate

9 min

Semantic Web

Understand the Semantic Web vision of machine-readable, interconnected data. Learn about RDF, SPARQL, linked data, and how TrustGraph implements Semantic Web principles.

#semantic-web#rdf#linked-data

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