Enabling Ontology & Knowledge Graph

Ontology and the knowledge graph are two different things with two different requirements. Knowing which is which saves a lot of unnecessary infrastructure.

What Works with No Extra Infrastructure

The ontology is stored relationally. Everything below works on a stock OpenMetadata deployment:

Ontology Studio View mode — graph, tree, filters, health, inspector
Ontology Studio Edit mode — concepts, typed relations, attributes, axioms, mappings, patterns, subsets, merges, change sets, impact previews
Relationship type management
Ontology Library pack installation
Ontology import and export (OWL, Turtle, N-Triples, JSON-LD, SKOS, CSV)
Glossary-scoped SPARQL (POST /api/v1/glossaries/{id}/sparql)
The ontology_describe MCP tool

What Needs the RDF Triple Store

Ontology Studio Query mode and the standalone SPARQL Playground
The 3D Knowledge Graph tab on tables
Catalog-wide SPARQL, graph exploration, and graph export
Inference and SHACL validation over the catalog
Graph insights — importance, centrality, communities, paths, recommendations, tag analytics
The sparql_query, entity_neighborhood, find_by_tag, and shacl_validate MCP tools
Linked Open Data IRI dereferencing

What Needs an LLM Provider

Ontology Studio AI mode — relationship, mapping, SPARQL, and domain-draft proposals

Requires RDF_ASK_COLLATE_ENABLED=true and a configured LLM provider.

Turning on RDF

The RDF knowledge graph is Beta and disabled by default. Turning it on for a large existing catalog adds meaningful load while the initial index runs. Test in a non-production environment first and schedule the initial index for a low-traffic window.

Full deployment instructions — Docker, Kubernetes, Helm, sizing, and troubleshooting — are in Enable RDF (Knowledge Graph). The short version:

Run Apache Jena Fuseki

Reachable from the OpenMetadata server. The repository ships one at docker/rdf-store.

Set the environment variables on both containers

The migrations container loads the ontology and shapes graphs and registers RdfIndexApp; the server container does everything else. If only one has RDF configured, installation fails with RdfRepository not initialized.

RDF_ENABLED=true
RDF_STORAGE_TYPE=FUSEKI
RDF_ENDPOINT=http://fuseki:3030/openmetadata
RDF_DATASET=openmetadata
RDF_REMOTE_USERNAME=admin
RDF_REMOTE_PASSWORD=<password>
RDF_BASE_URI=https://open-metadata.org/

Run the initial index

Settings → Applications → RDF Knowledge Graph Indexing → Run Now, or:

curl -X POST "$OM_HOST/api/v1/apps/trigger/RdfIndexApp" -H "Authorization: Bearer $TOKEN"

Verify

curl "$OM_HOST/api/v1/rdf/status" -H "Authorization: Bearer $TOKEN"

Expect enabled: true and projectionState: READY. REBUILDING means the index is still running; DEGRADED means something needs attention.

Trying It Locally

The repository ships a startup script that brings up the whole stack with RDF and Fuseki enabled:

./docker/run_local_docker_rdf.sh -m ui -d mysql -f true

It sets sensible development defaults, including materialized inference and CUSTOM as the default inference level.

Configuration Reference

All settings live under rdf: in openmetadata.yaml and are overridable by environment variable.

Core

Variable	Default	Purpose
`RDF_ENABLED`	`false`	Master switch. Every RDF code path is a no-op until true.
`RDF_STORAGE_TYPE`	`FUSEKI`	Backend. `QLEVER` is defined in the schema but not implemented — setting it fails at startup.
`RDF_ENDPOINT`	`http://localhost:3030/openmetadata`	Full URL ending in the dataset name. `RDF_REMOTE_ENDPOINT` is a deprecated fallback.
`RDF_DATASET`	`openmetadata`	Dataset name. Must match the path segment in `RDF_ENDPOINT`.
`RDF_REMOTE_USERNAME` / `RDF_REMOTE_PASSWORD`	`admin` / `admin`	Basic-auth credentials.
`RDF_BASE_URI`	`https://open-metadata.org/`	Base URI for minted resource IRIs.

Connection and Write Tuning

Variable	Default	Purpose
`RDF_CONNECT_TIMEOUT_MS`	`2000`	Connection timeout.
`RDF_REQUEST_TIMEOUT_MS`	`60000`	Per-request timeout.
`RDF_WRITE_MAX_RETRIES`	`2`	Retries for idempotent writes after the first attempt.
`RDF_WRITE_RETRY_INITIAL_BACKOFF_MS`	`250`	Initial retry backoff.
`RDF_WRITE_RETRY_MAX_BACKOFF_MS`	`2000`	Maximum retry backoff.
`RDF_BULK_ENTITY_BATCH_SIZE`	`100`	Entity models per bulk write.
`RDF_BULK_RELATIONSHIP_SOURCE_BATCH_SIZE`	`100`	Relationship sources per bulk write.
`RDF_BULK_LINEAGE_EDGE_BATCH_SIZE`	`50`	Lineage edges per SPARQL update.

Inference

Variable	Default	Purpose
`RDF_INFERENCE_ENABLED`	`false`	Enable inference on SPARQL queries.
`RDF_DEFAULT_INFERENCE_LEVEL`	`NONE`	`NONE`, `RDFS`, `OWL_LITE`, `OWL_DL`, or `CUSTOM`.
`RDF_MATERIALIZED_INFERENCE_ENABLED`	`false`	Use durable per-rule inferred named graphs instead of in-memory Jena models. Recommended past a small catalog.
`RDF_MAX_IN_MEMORY_INFERENCE_TRIPLES`	`100000`	Above this store size, inference requests fall back to direct execution — with a `warning`, and without inference.
`RDF_CACHE_INFERRED_TRIPLES`	`false`	Cache bounded in-memory inference models for 60 seconds.

Validation and Profile

Variable	Default	Purpose
`RDF_SHACL_VALIDATION_MODE`	`REPORT`	`OFF`, `REPORT`, or `ENFORCE_IMPORTS`. Never blocks entity writes.
`RDF_STRICT_OWL_PROFILE`	`true`	Reject authored axioms outside the supported OWL 2 DL profile.

Linked Data, AI, Federation

Variable	Default	Purpose
`RDF_DEREFERENCEABLE_IRIS`	`false`	Authenticated content-negotiated redirects for minted IRIs (`/api/v1/lod/entity/...`).
`RDF_ASK_COLLATE_ENABLED`	`false`	Expose the Ontology Studio AI mode. Manual authoring is unaffected.
`RDF_FEDERATION_ENABLED`	`false`	Master switch for `SERVICE` clauses to external SPARQL endpoints.
`RDF_FEDERATION_ALLOWED_ENDPOINTS`	`[]`	Allowlisted external endpoint URIs. Compared verbatim — trailing slashes matter.

Federation is a data-exfiltration surface. A SERVICE clause sends parts of your query — and potentially your data — to a third-party endpoint. It is disabled by default, and the allowlist is empty. Enable it only for endpoints you control or explicitly trust.

Indexing Application

RdfIndexApp configuration (Settings → Applications):

Setting	Default	Purpose
`entities`	`[]` (all supported)	Restrict the reindex to specific entity types.
`recreateIndex`	`true`	Clear the store before indexing.
`batchSize`	`100`	Entities per batch.
`producerThreads` / `consumerThreads`	`2` / `3`	Non-distributed reindex parallelism.
`queueSize`	`5000`	Internal queue size for non-distributed reindexing.
`useDistributedIndexing`	`true`	Partition-coordinated indexing across servers.
`partitionSize`	`10000`	Entities per partition. Smaller values create more partitions.

Only one reindex job can be active per cluster at a time.

Verifying the Whole Thing

# 1. RDF is on and the projection is ready
curl "$OM_HOST/api/v1/rdf/status" -H "Authorization: Bearer $TOKEN"

# 2. The graph has data
curl -X POST "$OM_HOST/api/v1/rdf/sparql" \
  -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
  -d '{"query":"SELECT (COUNT(*) AS ?n) WHERE { GRAPH <https://open-metadata.org/graph/knowledge> { ?s ?p ?o } }"}'

# 3. Inference rules are registered
curl "$OM_HOST/api/v1/rdf/rules" -H "Authorization: Bearer $TOKEN"

# 4. Shapes are loaded and the graph conforms
curl "$OM_HOST/api/v1/rdf/validate" -H "Authorization: Bearer $TOKEN" -D - -o /dev/null | grep OM-SHACL-Conforms

In the UI: Govern → Ontology Studio should show a Query tab, and any table should show a Knowledge Graph tab.

Deployment guide

Docker, Kubernetes, Helm, sizing, and troubleshooting.

Knowledge Graph

What you get once it is on.

Ontology & Knowledge Graph

Enabling Ontology & Knowledge Graph | OpenMetadata

Enabling Ontology & Knowledge Graph

What Works with No Extra Infrastructure

What Needs the RDF Triple Store

What Needs an LLM Provider

Turning on RDF

Trying It Locally

Configuration Reference

Core

Connection and Write Tuning

Inference

Validation and Profile

Linked Data, AI, Federation

Indexing Application

Verifying the Whole Thing

Next

Deployment guide

Knowledge Graph

​Enabling Ontology & Knowledge Graph

​What Works with No Extra Infrastructure

​What Needs the RDF Triple Store

​What Needs an LLM Provider

​Turning on RDF

​Trying It Locally

​Configuration Reference

​Core

​Connection and Write Tuning

​Inference

​Validation and Profile

​Linked Data, AI, Federation

​Indexing Application

​Verifying the Whole Thing

​Next

Deployment guide

Knowledge Graph

Enabling Ontology & Knowledge Graph

What Works with No Extra Infrastructure

What Needs the RDF Triple Store

What Needs an LLM Provider

Turning on RDF

Trying It Locally

Configuration Reference

Core

Connection and Write Tuning

Inference

Validation and Profile

Linked Data, AI, Federation

Indexing Application

Verifying the Whole Thing

Next