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Graph Insights

Once your catalog is a graph, graph algorithms become available to it. OpenMetadata exposes a set of them under /api/v1/rdf/insights — the kind of analysis that is trivial on a graph and painful on tables. All insight endpoints are admin-only and require RDF to be enabled.

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Importance

Ranks entities of one type by a composite importance score that blends two signals:
  • Usage percentile — real query activity from OpenMetadata’s usage data (weight 0.6).
  • Downstream lineage edge count — graph topology (weight 0.4).
Parameters: entityType (required, singular — table, dashboard, pipeline, mlmodel, …), window (daily, weekly, monthly; default daily), limit (1–100, default 20). Results are SPARQL JSON with usage percentile, downstream count, and the composite score.
Blending both signals is deliberate. Usage alone over-ranks the dashboard everyone opens out of habit; topology alone over-ranks a staging table nothing reads. Something that is both queried and heavily depended on is what you actually want to protect with a contract, an owner, and a test.

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Centrality (PageRank)

Walks lineage, tagging, and containment edges for the entity type, runs weighted PageRank, and persists scores to the named graph om:insights/centrality/{entityType}. Importance ranking blends these scores in for entities that have no query-usage data. Expensive by design — intended to run on a schedule, exposed for manual triggering.

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Communities (Louvain)

Extracts the lineage or tag-co-occurrence graph for the entity type, runs Louvain modularity optimization, and persists the partition to om:insights/communities/{graphType}/{entityType}. Each community is an om:Community resource with om:hasMember triples and a modularity score. graphType is lineage (default) or tagCoOccurrence. Communities surface the de facto structure of your platform — clusters of assets that move together — which is often not the structure your org chart or your domain model claims. Comparing the discovered partition against your declared domains is one of the more useful audits available.

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Shortest Lineage Path

Breadth-first search over the lineage graph (prov:wasDerivedFrom, om:upstream, om:downstream) returning the shortest path between two entity URIs. Each hop returns the URI, the predicate that connected it, and the entity’s om:* types. When no path exists within maxHops, the response says found: false rather than returning an empty list you have to interpret. This is the endpoint behind “how does this dashboard actually get its numbers” — and the honest answer to an incident review.

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Recommendations

Ranks every other entity by graph-topology similarity to a seed: overlap on tags, glossary terms, and direct lineage neighbors.
Pure SPARQL, no precomputation — so it is always current, and it costs something. limit is 1–50, default 10. Note the weighting: shared lineage counts double a shared tag. Two tables that share a tag might merely both be PII; two tables that share an upstream are genuinely about the same thing.
This is a different notion of “similar” from semantic search, which compares embeddings of names and descriptions. Structural similarity finds assets that behave alike; semantic similarity finds assets that read alike. They disagree usefully.

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Tag Analytics

Co-occurrence is a classification-hygiene tool: two tags that almost always appear together are usually one tag with a naming problem. Glossary reach identifies your genuinely cross-cutting concepts — the ones where a definition change is an organization-wide event rather than a team-local one.

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Semantic Search over the Graph

Vector-based search delegating to OpenSearch’s vector capabilities.
These endpoints require OpenSearch, not Elasticsearch. Core RDF indexing, SPARQL, graph exploration, and every other insight endpoint work with either.

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SQL → SPARQL Translation

Translate a SQL statement into the equivalent SPARQL against the knowledge graph, or translate and execute in one call. Useful as an on-ramp for teams fluent in SQL, and as a way to see how a familiar query maps onto graph patterns.

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Linked Open Data

With RDF_DEREFERENCEABLE_IRIS=true:
Performs authenticated, content-negotiated redirection for OpenMetadata-minted IRIs, so a linked-data client can follow an entity IRI and get RDF back. This is what makes the IRIs in your exports resolvable rather than merely unique.

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Knowledge Graph API

The complete endpoint reference.

For AI Agents

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