Shapes Constraint Language (SHACL) is a powerful language for validating RDF (Resource Description Framework) knowledge graphs against a set of conditions. These conditions are known as shapes and can be used by data governance professionals to streamline consistent data quality across the organization’s data ecosystem. SHACL was developed by the World Wide Web Consortium (W3C) into the open, industry standard for data quality assurance in data-centric, semantic technologies and linked data projects.

In Fluree’s graph database, users leverage policy-based SHACL to express constraints like required properties, data types, cardinality, and closed class shapes.

What is the difference between OWL and SHACL?

OWL and SHACL are typically compared as both ways in which to manage and maintain datasets, but their core competencies differ. OWL helps with inference; SHACL helps with validation. Let’s break it down further: 

• OWL is primarily designed for defining and reasoning about ontologies. It provides a rich set of constructs for describing the relationships between concepts in a domain, enabling sophisticated inferencing capabilities about the types of entities and their relationships. OWL is used to create complex domain models and to infer new knowledge from existing data.

• SHACL, on the other hand, is focused on data validation. It allows developers and data architects to define constraints on the structure and content of RDF graphs, ensuring that the data adheres to specified patterns, value ranges, or other criteria. While OWL focuses on enabling inference, SHACL is specifically tailored for validation, offering a more direct approach to enforcing data quality rules.

What does SHACL look like?

SHACL uses a graph-based syntax, where shapes are defined as RDF graphs. Let’s take a look at a simple example: let’s say that we want to ensure that all values assigned to the property “schema:birthday” are enforced as xsd:dateTime (in plain english: birthdays are formatted as valid dates and times). In Fluree, we could insert our constraint in a ledger like this:

{
  "@context": "https://ns.flur.ee",
  "ledger": "ledger/data-type",
  "insert": {
    "@id": "ex:UserShape",
    "@type": ["sh:NodeShape"],
    "sh:targetClass": { "@id": "ex:Person" },
    "sh:property": [
      {
        "sh:path": { "@id": "schema:birthDate" },
        "sh:datatype": { "@id": "xsd:dateTime" }
      }
    ]
  }
}

What are the benefits of using SHACL for data validation?

Data governance is increasingly becoming critical, as organizations need to leverage clean, available, and trusted data on a day-to-day basis. By employing a global framework for data validation, organizations can save an immense amount of time and effort in standardizing data for analytics and re-use. 

SHACL offers several benefits for data validation:

• Flexibility and Expressiveness: SHACL can express a wide range of constraints, including property existence, value type, value range, cardinality, and more complex conditions.
• Scalability: SHACL validation can be efficiently implemented, making it suitable for large datasets.
• Standardization: As a W3C standard, SHACL ensures interoperability and consistency across different tools and platforms. This helps make not only the data interoperable, but also the rules that govern data consistency and validation interoperable. 
• Declarative Approach: SHACL allows the expression of validation rules in a declarative manner, separating the rules from their execution, which can improve maintainability and understandability of data constraints.

What kinds of quality constraints can SHACL enforce?

SHACL can enforce a variety of quality constraints, including:

• Structural Constraints: Ensuring that data adheres to a specific schema or model, such as required properties, permissible property values, or specific class hierarchies.
• Value Constraints: Limiting the values that can be taken by properties, including data types, value ranges, and pattern matching.
• Cardinality Constraints: Defining the minimum and maximum occurrences of properties.
• Logical Constraints: Applying logical conditions to properties and values, such as equality or inequality, and combinations thereof through logical operators.

How does SHACL improve enterprise data at scale?

For enterprises dealing with vast amounts of data, maintaining data quality is paramount. SHACL provides a robust framework for ensuring that data across the organization conforms to agreed-upon standards and models. At scale, SHACL helps in:

• Automating Data Validation: Automated tools can leverage SHACL shapes to validate data as it is ingested, updated, or transformed, ensuring continuous data quality without manual intervention.
• Enforcing Data Governance: SHACL shapes can embody data governance policies, ensuring compliance with internal and external data standards and regulations.
• Improving Data Interoperability: By enforcing standardized data models and structures, SHACL facilitates data sharing and interoperability both within the enterprise and with external partners.
• Enhancing Data Quality: Consistent application of SHACL validation helps identify and rectify data quality issues early, reducing errors and improving the reliability of data-driven decisions.

SHACL is a critical tool for anyone involved in the management, curation, and utilization of RDF data. Its ability to enforce complex data quality rules in a flexible and scalable manner makes it indispensable for maintaining high-quality, interoperable datasets in the age of big data and the semantic web.

Try SHACL out with Fluree! 

Head on over to our cookbook documentation for some examples that you can test out with your free Fluree cloud account! 

*special thanks to Buck Flannigan and Laurent Bégin for their contributions to this article