The Importance of Combining Semantic and Structural Validation for Ontologies in Application Engineering

In the field of ontology engineering, the goal is not just to build ontologies that are logically consistent, but also to ensure that they are usable in real-world applications. This dual perspective—ensuring both semantic integrity and structural consistency—is key to creating ontologies that are useful, maintainable, and interoperable.

One of the core elements of ontology design is the use of annotation properties, such as rdfs:label, to provide human-readable descriptions of resources. While essential for making ontologies accessible, the way these labels are applied can sometimes raise concerns about both semantic accuracy and structural compliance.

The Challenge with Labels in Ontologies

A property like rdfs:label is commonly used in many ontologies to assign a human-readable name to a resource. However, the lack of restriction on the types of values that annotation properties like rdfs:label can hold may lead to inconsistencies. In theory, rdfs:label should only be used with literals (i.e., textual values), but in practice, it is possible to apply it to other types of data, such as URIs or even blank nodes. This misuse can lead to issues when ontologies are integrated with other systems, particularly if applications expect a strict definition of what the label property should contain.

The absence of domain and range constraints for many annotation properties in OWL, especially those like rdfs:label, leaves them open to flexible usage, potentially leading to misuse or inconsistencies in how annotations are applied across different datasets.

Why Both Semantic and Structural Validation Matter

To ensure ontologies are robust, it’s crucial to address both semantic validation and structural validation. While semantic validation ensures that the ontology correctly represents domain knowledge, structural validation ensures that the ontology follows a formalized structure that aligns with the requirements of various applications.

This is where SHACL (Shapes Constraint Language) comes in, providing a powerful tool for validating the structure of an RDF graph. SHACL allows us to specify constraints that govern how resources should be related, how annotation properties should be applied, and how the data should be formatted. However, SHACL focuses primarily on structural correctness, not semantic integrity. This is where the need for a combined validation approach arises, especially when ontologies serve as software artifacts that power application logic and service delivery.

Combining SHACL and Semantic Validation for Ontology Engineering

By combining SHACL with semantic validation techniques (e.g., OWL reasoning tools), we can create a more comprehensive validation framework that considers both the logical correctness of the ontology and its structural adherence to application needs. This is particularly important in shared practices for ontology engineering, where ontologies must adhere not only to formal logical standards but also to the constraints imposed by the applications consuming those ontologies.

For example, when creating an ontology that will be used by a healthcare application, we may define a constraint that the rdfs:label property must always contain a literal string. At the same time, SHACL can enforce that this label is only applied to certain types of resources, such as instances of Person or MedicalRecord, ensuring both semantic accuracy and structural compliance.

This dual validation approach is essential when ontologies are used as software artifacts, where they need to interact with other data sources and applications. By incorporating SHACL rules to enforce structural integrity, we ensure that ontologies can be easily integrated into software systems without compromising their quality or performance.

Ontology Engineering as a Shared Practice for Validating Artifacts

In a collaborative context, where multiple teams may be developing ontologies that need to work together or integrate with existing systems, it becomes critical to establish shared practices for validation. SHACL provides a way to create specific, non-generic validation rules for each application’s needs, ensuring that the ontology can be used as an artifact in software services while maintaining its semantic richness.

For instance, if you are building a suite of applications that consume an ontology, SHACL can be used to define specific rules about how data should be structured for each application’s needs. In this case, SHACL is not just about validating RDF graphs but also about ensuring that the semantic intent of the ontology aligns with the software constraints needed to deliver those services.

Through this approach, we can ensure that the ontologies we create are not just technically sound but also optimized for real-world use cases. By combining semantic validation (through OWL reasoning) and structural validation (through SHACL), we can produce ontologies that are both logically correct and structurally compatible with the systems that rely on them.

Use Case: Data Integration and Application Interoperability

In scenarios where multiple ontologies must be integrated or shared across different applications, SHACL can ensure that the structural integrity of the data is maintained. This is crucial for data interoperability and for ensuring that different systems can use the ontology consistently.

For example, when integrating ontologies from different domains (e.g., medical, geographical, and environmental), SHACL can validate that the relationships between resources in the merged ontology meet the requirements of all consuming applications. At the same time, semantic validation ensures that these relationships make sense in terms of the domain knowledge being represented.

Conclusion

Combining semantic validation and structural validation is essential for building high-quality, interoperable ontologies. SHACL helps ensure that the structure of RDF graphs aligns with the requirements of the systems using them, while semantic validation ensures that the data remains logically consistent and meaningful.

This dual approach is particularly valuable in the context of ontology engineering as a shared practice, where ontologies must be both logically sound and fit for purpose in the applications that will consume them. By leveraging both SHACL and semantic validation techniques, we can ensure that ontologies are ready to serve as high-quality software artifacts—validated both for their internal consistency and for their use in diverse applications.

Through this approach, we can not only validate human-readable labels but also ensure that the ontology’s metadata, structure, and logic are aligned with the needs of both the ontology and the applications that will use it.