What is SHACL?

Knowledge graphs are only as valuable as the data they contain. Without validation, bad data - missing properties, wrong types, broken links - creeps in silently and undermines trust in downstream analytics and AI.

SHACL solves this by letting you define machine-readable quality rules. When new data is ingested, SHACL shapes validate it automatically and produce detailed violation reports. This shifts data quality from manual review to automated governance.

In regulated industries, SHACL provides auditable proof that data meets defined standards - essential for compliance with frameworks like BCBS 239, GDPR data accuracy requirements, and clinical data integrity rules.

SHACL defines shapes that describe constraints on RDF nodes. A shape targets a set of nodes (e.g., all instances of ex:Person) and declares constraints they must satisfy:

Property constraints: “Every Person must have exactly one name” (sh:minCount 1; sh:maxCount 1).

Value type constraints: “The birthDate property must be an xsd:date” (sh:datatype xsd:date).

Pattern constraints: “Email must match a regex pattern” (sh:pattern).

Structural constraints: “Every Order must link to at least one LineItem that has a positive quantity” (nested shapes).

When you validate data against SHACL shapes, you get a validation report listing every violation with the offending node, the constraint that failed, and a human-readable message. This makes debugging straightforward.

Financial data quality: A bank defines SHACL shapes for its FIBO-aligned knowledge graph. Every new data feed is validated on ingestion, catching issues like missing LEI codes or invalid entity classifications before they reach downstream risk models.

Master data management: A manufacturer uses SHACL to enforce product data standards across 12 factories. When a factory submits a new product record missing required attributes, the validation report tells them exactly what to fix.

Open data publishing: The EU’s data portal uses SHACL shapes based on the DCAT-AP profile to validate dataset metadata submitted by member states, ensuring consistent discoverability across borders.