How is a knowledge graph different from a relational database?

Relational databases imply relationships through foreign keys and JOINs. Knowledge graphs make relationships first-class citizens — explicitly typed and directly traversable. That’s the difference between an LLM guessing at a JOIN and an LLM following a declared path from entity to entity.

How is Fluree different from Neo4j and other graph databases?

Fluree is W3C-native (RDF, JSON-LD, SPARQL, OWL, SKOS), embeds security policies in the data, ships immutable provenance with time-travel queries, unifies structured and unstructured content in one graph, and includes native hybrid retrieval (graph + BM25 + HNSW) for AI workloads — none of which are available out of the box in property-graph systems.

Do we need to replace our existing databases?

No. Fluree connects to systems of record through its Sense and CAM pipelines. Data stays where it lives; Fluree creates the governed semantic layer that unifies it for AI, analytics, and search.

How long does it take to stand up an enterprise knowledge graph?

With Fluree’s AI-assisted construction, most teams move from fragmented schemas to a working graph with meaningful queries in 4–8 weeks. Start with one domain, prove value, and expand.

Can a knowledge graph handle both structured and unstructured data?

With Fluree, yes. Sense classifies structured data from databases and SaaS systems; CAM extracts entities and relationships from PDFs, audio, and video. Both land in the same graph against the same semantic model.

How does a knowledge graph improve AI accuracy?

Knowledge graphs give AI structured, relationship-aware context. That enables GraphRAG, where the model traverses explicit entity connections instead of guessing from disconnected text fragments — pushing enterprise retrieval accuracy from 60–70% (vector RAG) to 95%+ with governed provenance.

The data layer your AI actually understands.

Q: What is an enterprise knowledge graph?

An enterprise knowledge graph is a semantic data layer that represents business information as entities connected by typed relationships. Instead of storing data in disconnected tables, it encodes meaning directly in the structure — so AI, analytics, and applications can traverse real connections across the enterprise.

An enterprise knowledge graph connects your data the way your business works — entities, relationships, and meaning — so agents traverse real connections instead of guessing at JOINs. Fluree ships one that is governed by default, verifiable to every fact, and AI-ready from day one.

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What Is An Enterprise Knowledge Graph

Relationships are first-class. Meaning lives in the data.

A knowledge graph represents business information as entities connected by typed relationships. Instead of implying connections through foreign keys and JOINs, it encodes them directly — a single semantic layer over every system, in every format.

When an agent answers a question like "Which customers have compliance risk exposure above $1M?" it doesn’t stitch tables together — it follows a declared path from Customer to Account to Transaction to ComplianceRisk.

That’s why the knowledge graph has become the bottleneck for enterprise AI — not the model. Fluree builds the graph that’s governed, verifiable, and ready to serve every agent downstream.

How We Build It

From scattered data to connected intelligence.

Four stages. One platform. Model, map, connect, activate — the full path from raw source to governed knowledge graph.

Model — define your business vocabulary

Start in Fluree ITM with your ontology, taxonomies, and controlled vocabulary. Use AI-assisted discovery from existing schemas, begin with an upper ontology like GIST or FIBO, or model from scratch — no code required.

Map — classify and link your data

Fluree Sense classifies structured data against the model; Fluree CAM extracts entities and relationships from documents, audio, and video. Entity resolution produces golden records with lineage.

Connect — persist in Fluree Core

Everything lands in Fluree Core as RDF triples with typed relationships, embedded security, immutable provenance, and hybrid BM25 + HNSW search in a single engine.

Activate — serve AI, analytics, and apps

Query via natural language, SPARQL, REST, or MCP. Power GraphRAG, conversational analytics, and governed agents with answers that trace back to the source.

Why Fluree’s Knowledge Graph Is Different

Six capabilities other graph databases
bolt on. We build in.

Built on open web standards, not a proprietary query language

Fluree stores data as RDF triples in JSON-LD, modeled with OWL and SKOS, queried with SPARQL. Your graph is portable, interoperable, and lock-in-free — it speaks the language the AI ecosystem already knows.

RDF + JSON-LD data model
SPARQL, REST, and natural language in one engine
No proprietary query language to hire for

Structured and unstructured data unified under one schema

Fluree Sense classifies structured data. Fluree CAM extracts entities and relationships from documents, audio, and video. Both land in the same governed graph — so one traversal moves from a customer record to a contract clause to a compliance obligation.

Structured: databases, SaaS, warehouses, APIs
Unstructured: PDFs, audio, video, contracts
One semantic model over every source

Graph, BM25, and vector search in one engine

Fluree Core serves graph traversal, full-text, and HNSW vector search from the same engine against the same schema. The graph is self-describing, so GraphRAG, natural language queries, and MCP-connected agents work without a stack of glue code.

Native MCP server for agent workflows
HNSW vectors + BM25 in the same query
GraphRAG reaches 95%+ retrieval accuracy

Side by Side

Traditional graph databases vs.
Fluree.

A direct capability comparison between traditional graph databases and Fluree’s knowledge graph platform.

Capability	Traditional Graph databases	Fluree Knowledge Graph
Data model	Property graph or pure RDF	W3C RDF + JSON-LD, fully interoperable
Query languages	Cypher, Gremlin, or SPARQL	SPARQL + REST + natural language
Unstructured data	Separate pipeline or plugin	Native via Fluree CAM entity extraction
Security model	App layer or IAM only	Policy in the data, enforced at query time
Provenance	Not supported	Time-travel with cryptographic audit
Hybrid retrieval	Vector store bolted on	Graph + BM25 + HNSW in one engine
AI / MCP integration	Manual glue code	Native MCP server
Graph construction	Manual ETL + modeling	AI-assisted via Sense + CAM + ITM
GraphRAG readiness	Custom implementation required	Native — 95%+ accuracy