Knowledge Graphs with ArcadeDB

The Enterprise Knowledge Crisis

The average enterprise manages over 400 distinct data sources. Knowledge workers spend 30% of their day searching for information — and still fail to find what they need 50% of the time. Meanwhile, 80% of enterprise data is unstructured: documents, emails, wikis, Slack threads, and meeting transcripts that traditional databases cannot meaningfully index.

The result? Siloed knowledge, duplicated work, and AI systems that hallucinate because they lack organizational context. Knowledge graphs solve this by turning scattered information into a connected, queryable fabric — but traditional graph databases only handle one piece of the puzzle.

What a Knowledge Graph Looks Like

-- Schema: entities + typed relationships
CREATE VERTEX TYPE Concept
CREATE VERTEX TYPE Document
CREATE VERTEX TYPE Person
CREATE VERTEX TYPE Organization

CREATE EDGE TYPE MENTIONS
CREATE EDGE TYPE RELATES_TO
CREATE EDGE TYPE AUTHORED_BY
CREATE EDGE TYPE CITES
CREATE EDGE TYPE BELONGS_TO

-- Multi-hop reasoning: find all concepts
-- related to a topic through any path
MATCH (start:Concept {name: 'Machine Learning'})
      -[:RELATES_TO*1..4]-(related:Concept)
RETURN related.name, related.description,
       min(length(path)) AS distance
ORDER BY distance

Graph Relationships: The Foundation of Knowledge

A knowledge graph models the world as entities (concepts, documents, people, organizations) and relationships between them (mentions, relates-to, authored-by, cites). This structure enables multi-hop reasoning — the ability to discover indirect connections that keyword search will never find.

"What research papers cite work by authors in our AI team that relate to concepts mentioned in our latest product roadmap?" — this question traverses four relationship types across three entity types. In a relational database, it requires multiple JOINs and often times out. In ArcadeDB, it's a single Cypher or SQL query that returns in milliseconds.

ArcadeDB supports Cypher (OpenCypher), SQL, and Gremlin — choose the query language your team already knows, or use all three against the same graph.

Semantic Search with JVector Embeddings

Keyword search fails when users don't know the exact terminology. A developer searching for "how to handle errors in the payment service" won't find a document titled "Exception Management in Billing Module" — even though it's exactly what they need.

ArcadeDB's native JVector engine stores vector embeddings directly on knowledge graph nodes. JVector uses a DiskANN + HNSW hybrid algorithm with SIMD acceleration for near-exact nearest-neighbor search at millisecond latency. Embeddings are computed by your model of choice (OpenAI, Cohere, open-source) and stored alongside the entity's graph relationships, metadata, and full-text content.

The result: search by meaning, not just keywords, and immediately explore the graph context around every result.

-- Store embeddings on knowledge nodes
CREATE PROPERTY Document.embedding VECTOR(1536)
CREATE INDEX ON Document (embedding)
  TYPE HNSW SIMILARITY cosine

-- Semantic search: find documents by meaning
SELECT title, summary,
       vectorDistance(embedding, [0.012, -0.034, ...])
       AS similarity
FROM Document
WHERE embedding NEAR [0.012, -0.034, ...]
  LIMIT 20

-- Enrich with graph context
MATCH (d:Document)
      WHERE d.title IN ['...top results...']
MATCH (d)-[:AUTHORED_BY]->(author:Person),
      (d)-[:MENTIONS]->(concept:Concept),
      (d)<-[:CITES]-(citing:Document)
RETURN d.title, author.name,
       collect(concept.name) AS topics,
       count(citing) AS citation_count

-- GraphRAG: retrieve context for LLM
-- Step 1: Vector search for relevant docs
LET $seeds = (
  SELECT @rid, title, content, embedding
  FROM Document
  WHERE embedding NEAR :queryEmbedding
  LIMIT 10
)

-- Step 2: Expand graph neighborhood
SELECT seed.title AS source,
       concept.name AS concept,
       concept.definition AS definition,
       related.title AS related_doc,
       related.summary AS related_summary
FROM (
  TRAVERSE out('MENTIONS'), out('RELATES_TO'),
           in('CITES'), out('BELONGS_TO')
  FROM $seeds
  WHILE $depth <= 2
)
WHERE @type IN ['Concept', 'Document']

GraphRAG: The Best Context for Your LLM

Retrieval-Augmented Generation (RAG) grounds LLM responses in your data. But vanilla RAG — vector search alone — misses structural context. It retrieves similar documents but can't answer "how are these concepts related?" or "what are the dependencies between these components?"

GraphRAG solves this by combining vector retrieval with graph traversal. The vector search finds semantically relevant seed documents; then graph traversal expands to related concepts, definitions, cited papers, and connected entities. The LLM receives not just relevant text but structured relationships that dramatically improve answer accuracy and reduce hallucinations.

Research shows hybrid graph + vector retrieval improves factual accuracy by up to 2.8x compared to vector-only approaches. With ArcadeDB, you implement GraphRAG with a single database query — no orchestration layer stitching together a vector database and a graph database.

Full-Text Search Meets Graph Context

Not every query needs semantic understanding. Sometimes users search for a specific error code, a product name, or an exact phrase. ArcadeDB's built-in full-text indexing supports keyword search, boolean operators, fuzzy matching, and phrase search — integrated directly into graph queries.

The powerful combination: use full-text search to find exact matches, vector search to find semantically similar results, and graph traversal to expand context around both. This hybrid retrieval pattern consistently outperforms any single approach.

Full-text indexes in ArcadeDB support stemming, stop-word removal, language-specific analyzers, and relevance scoring — all queryable through SQL or Cypher alongside graph traversals and vector similarity.

-- Full-text index on document content
CREATE INDEX ON Document (content)
  TYPE FULL_TEXT

-- Hybrid retrieval: full-text + graph
SELECT title, summary,
       search_score(content) AS relevance
FROM Document
WHERE content CONTAINSTEXT
  'connection pooling timeout'
ORDER BY relevance DESC
LIMIT 10

-- Expand: who authored these docs, what
-- concepts do they cover, what's related?
MATCH (d:Document)
      WHERE d.content CONTAINSTEXT
      'connection pooling timeout'
MATCH (d)-[:AUTHORED_BY]->(a:Person),
      (d)-[:MENTIONS]->(c:Concept)
RETURN d.title, a.name AS author,
       a.team AS team,
       collect(c.name) AS concepts

-- Track knowledge freshness over time
CREATE TIMESERIES TYPE DocActivity
  TIMESTAMP ts PRECISION MILLISECOND
  TAGS (doc_id STRING, action STRING)
  FIELDS (user_id STRING, views LONG,
          edits LONG, confidence DOUBLE)
  RETENTION 365 DAYS

-- Find stale knowledge: docs with declining
-- engagement and no edits in 90 days
SELECT d.title, d.department,
       ts.rate(da.views, da.ts) AS view_trend,
       ts.last(da.edits, da.ts) AS last_edit_count
FROM Document AS d
TIMESERIES d -> DocActivity AS da
  FROM '2025-09-01' TO '2026-02-22'
WHERE ts.rate(da.views, da.ts) < 0
  AND ts.last(da.edits, da.ts) = 0
ORDER BY view_trend ASC

Temporal Knowledge: When Facts Change Over Time

Knowledge isn't static. API documentation becomes outdated. Compliance regulations evolve. Product specifications change with every release. A knowledge graph without temporal awareness serves stale information — which is worse than no information at all.

ArcadeDB's native time-series engine tracks document activity (views, edits, citations) over time, enabling queries that factor in knowledge freshness. Surface documents with declining engagement for review. Detect when a concept's definition has drifted across sources. Identify which teams produce the most-consulted documentation.

Combined with graph traversal, time series lets you answer questions like: "Show me all active compliance documents authored by the legal team in the last 6 months, along with the regulations they reference and any newer versions that supersede them."

Automated Knowledge Graph Construction

Building a knowledge graph used to require months of manual entity extraction and relationship mapping. Modern LLM-based pipelines automate this process: feed unstructured documents into an LLM to extract entities, relationships, and metadata, then store everything in ArcadeDB with a single batch insert.

ArcadeDB's multi-model architecture is uniquely suited for this workflow. When the LLM extracts entities, they become graph vertices with typed relationships. The original document text gets full-text indexed for keyword search. The LLM-generated embeddings are stored as vector properties for semantic search. And document metadata (source, date, confidence scores) is stored as flexible document properties — no schema migration required.

Integrate with LangChain, LlamaIndex, or any LLM framework. ArcadeDB's SQL, Cypher, and REST APIs work with every major extraction pipeline.

-- Ingest LLM-extracted entities + relations
-- from an unstructured document

-- 1. Store the source document
INSERT INTO Document SET
  title = 'API Rate Limiting Guide',
  content = '...full text...',
  embedding = [0.023, -0.041, ...],
  source = 'confluence',
  extracted_at = now(),
  confidence = 0.94

-- 2. Create extracted concepts
INSERT INTO Concept SET
  name = 'Rate Limiting',
  definition = 'Controlling the rate of
    requests a client can make...',
  embedding = [0.018, -0.055, ...]

-- 3. Link document to concepts
CREATE EDGE MENTIONS
  FROM (SELECT FROM Document
        WHERE title = 'API Rate Limiting Guide')
  TO   (SELECT FROM Concept
        WHERE name = 'Rate Limiting')
  SET relevance = 0.95,
      context = 'primary topic'

-- Agentic AI: context retrieval for
-- an autonomous agent deciding next steps

-- Agent needs: "What do I know about this
-- customer's infrastructure and recent
-- incidents related to their question?"

MATCH (c:Customer {id: 'acme-corp'})
      -[:USES]->(p:Product)
      -[:HAS_COMPONENT]->(comp:Component)
OPTIONAL MATCH
      (comp)<-[:AFFECTS]-(inc:Incident)
      WHERE inc.created > date() - duration('P90D')
OPTIONAL MATCH
      (comp)-[:DOCUMENTED_IN]->(doc:Document)
      WHERE doc.embedding NEAR :agentQueryVec
RETURN c.name, p.name AS product,
       comp.name AS component,
       collect(inc.summary) AS recent_incidents,
       collect(doc.title) AS relevant_docs

Grounding for Agentic AI

As AI agents move from prototype to production, they need structured, trustworthy context to make autonomous decisions. A support agent answering a customer question needs to know the customer's products, recent incidents, relevant documentation, and team relationships — and it needs this in milliseconds, not minutes.

Knowledge graphs serve as the shared memory and coordination layer for multi-agent systems. Unlike flat vector stores, a knowledge graph provides entity disambiguation (which "billing" does this refer to?), access control (is this agent authorized to see this data?), and relationship context (what connects this customer to this incident?).

ArcadeDB provides this context through a single query that combines graph traversal with vector similarity and full-text matching — giving agents precise, relationship-aware grounding that reduces hallucinations and enables autonomous reasoning.

Knowledge Graph Applications

Every industry has knowledge that's trapped in silos. A knowledge graph frees it — and a multi-model knowledge graph makes every model available in a single query.

Application	Graph	Vectors	Full-Text	Time Series
Enterprise Search	Dept, team, project context	Semantic similarity	Keyword + exact match	Freshness scoring
GraphRAG / LLM Grounding	Multi-hop context	Seed document retrieval	Exact term lookup	Recency weighting
Drug Discovery	Protein-drug-disease links	Molecular similarity	Literature search	Trial timeline tracking
IT Operations / AIOps	Service dependency map	Log anomaly detection	Error message search	Metric correlation
Regulatory Compliance	Regulation → policy → control	Clause similarity	Exact statute lookup	Version tracking
Supply Chain Intelligence	Supplier → component → product	Alternative sourcing	Contract search	Delivery trend analysis
Cybersecurity Threat Intel	Attack chain mapping	IOC similarity	CVE lookup	Attack pattern timing
Agentic AI Memory	Entity → action → outcome	Context retrieval	Tool/API lookup	Interaction history

Why ArcadeDB for Knowledge Graphs

Most knowledge graph implementations require stitching together 3-4 systems: a graph database for relationships, a vector database for embeddings, a search engine for full-text, and a time-series database for temporal context. ArcadeDB replaces them all.

Capability	ArcadeDB	Neo4j	Neptune	Stardog
Native Graph	✓ Property Graph	✓ Property Graph	✓ Property + RDF	✓ RDF only
Native Vector Search	✓ JVector (HNSW + DiskANN)	✓ Vector index (5.x)	✓ Neptune Analytics	✓ Similarity search
Native Full-Text Search	✓ Built-in	✓ Lucene-based	○ Via OpenSearch	✓ Built-in
Native Time Series	✓ Full engine	✗	✗	✗
Document Model	✓ Native JSON documents	○ Properties only	○ Properties only	✗ RDF triples
Query Languages	SQL + Cypher + Gremlin	Cypher	Gremlin + SPARQL	SPARQL + GraphQL
Deployment	Anywhere (self-hosted)	AuraDB or self-hosted	AWS only	Cloud or self-hosted
License	Apache 2.0 (forever)	GPL / Commercial	Proprietary	Proprietary

Open Source. Apache 2.0. Forever.

Your knowledge graph is the backbone of your AI systems. We believe you should own it completely — the data, the database, and the freedom to deploy it anywhere. ArcadeDB is licensed under Apache 2.0, and we've made a public commitment: we will never change it.

No surprise license switches. No "open core" gotchas. No cloud-only features held hostage. Build your knowledge graph on a foundation you can trust. Read our commitment →

Enterprise Deployment Success

"We built our enterprise knowledge graph on ArcadeDB, integrating 15 years of technical documentation, research papers, and internal wikis. The combination of graph traversal, semantic search, and full-text indexing in a single database eliminated three separate systems we were maintaining before. Search accuracy improved dramatically, and our GraphRAG pipeline went from 4 services to 1."

— Director of Engineering, Fortune 500 Technology Company
(Details limited by confidentiality agreement)

Impact Metrics:

85% improvement in search relevance scores
40% reduction in time-to-find-information
3.2M entities with 28M relationships in knowledge graph
Sub-100ms query response for semantic + graph search
3 separate systems (graph DB + vector DB + search engine) replaced by 1

Industries Using Knowledge Graphs

Technology: Internal documentation, API discovery, codebase navigation
Healthcare: Clinical knowledge, drug interactions, patient pathways
Financial Services: Regulatory compliance, risk assessment, due diligence
Life Sciences: Drug discovery, genomics, clinical trials
Government: Intelligence analysis, policy tracking, citizen services
Manufacturing: Product knowledge, supply chain, maintenance procedures
Legal: Case law, contract analysis, regulatory mapping
Education: Curriculum mapping, learning paths, research networks

Knowledge Graphs & Intelligent Search