ChemDCAT-AP: Enabling Semantic Interoperability with a Contextual Extension of DCAT-AP

Cross-domain data integration drives interdisciplinary data reuse and knowledge transfer across domains. However, each discipline maintains its own metadata schemas and domain ontologies, employing distinct conceptual models and application profiles, which complicates semantic interoperability. The W3C Data Catalog Vocabulary (DCAT) offers a widely adopted RDF vocabulary for describing datasets and their distributions, but its core model is intentionally lightweight. Numerous domain-specific application profiles have emerged to enrich DCAT’s expressivity, the most well-known DCAT-AP for public data. To facilitate cross-domain interoperability for research data, we propose DCAT-AP PLUS, a DCAT Application Profile (P)roviding additional (L)inks to (U)se-case (S)pecific context (DCAT-AP+). This generic application profile enables a comprehensive representation of the provenance and context of research data generation. DACT-AP+ introduces an upper-level layer that can be specialized by individual domains without sacrificing compatibility. We demonstrate the application of DCAT-AP+ and a specific profile ChemDCAT-AP to showcase the potential of data integration of the neighboring disciplines chemistry and catalysis. We adopt LinkML, a YAML-based modeling framework, to support schema inheritance, generate domain-specific subschemas, and provide mechanisms for data type harmonization, validation, and format conversion, ensuring smooth integration of DCAT-AP+ and ChemDCAT-AP within existing data infrastructures.

💡 Research Summary

The paper addresses the limitation of the W3C Data Catalog Vocabulary (DCAT‑AP) in capturing the rich experimental context required for interdisciplinary research, particularly in chemistry and catalysis. While DCAT‑AP provides a lightweight, widely adopted RDF schema for describing datasets and their distributions, its core model lacks the granularity needed to represent provenance, experimental conditions, and domain‑specific entities such as chemical structures, reactions, and analytical methods. To bridge this gap, the authors propose a two‑layer extension architecture. The first layer, DCAT‑AP+, is a generic, domain‑agnostic augmentation that introduces a provenance‑centric pattern based on the W3C PROV‑O ontology. It adds classes and properties for activities, entities, and agents, enabling detailed description of data‑generating processes (e.g., measurements, simulations) and their inputs/outputs.

Building on DCAT‑AP+, the second layer is a concrete application profile for chemistry and catalysis called ChemDCAT‑AP. ChemDCAT‑AP imports the generic extension and enriches it with chemistry‑specific vocabularies (ChEBI, CHEMINF, CHMO) and data types (InChI, SMILES, reaction conditions, NMR, GC, etc.). The authors implement the entire stack using LinkML, a YAML‑based modeling framework that can translate a single schema into multiple machine‑readable artefacts (Python/Pydantic classes, JSON Schema, SHACL shapes, RDF/JSON‑LD).

Methodologically, the team first automated the conversion of the official DCAT‑AP 3.0 SHACL shapes into a LinkML schema, preserving original IRIs and handling union‑type properties via LinkML’s any_of construct (with a temporary restriction on date unions due to current tool limitations). They then programmatically extended this base schema with PROV‑O‑aligned activity patterns, creating a DataGeneratingActivity subclass and associated properties such as has_input_entity, has_output_entity, and carried_out_by. Finally, they manually crafted ChemDCAT‑AP by adding domain‑specific classes and mapping them to established ontologies.

The implementation is hosted on GitHub with a full CI/CD pipeline: automated schema validation, generation of documentation, and testing of example data instances. Validation leverages LinkML‑runtime and pyDantic for instance‑level constraints, while SHACL validation ensures RDF consistency. The authors demonstrate the practical utility of ChemDCAT‑AP by integrating it into the NFDI4Chem Search Service. They model a Minimum Information for a Chemical Investigation (MIChI) use case for NMR spectroscopy, showing that the metadata can be serialized as RDF, JSON‑LD, or SHACL and seamlessly queried across the portal.

Results indicate that ChemDCAT‑AP retains full compatibility with existing DCAT‑AP deployments while providing a richer, provenance‑aware description of chemical and catalytic datasets. The approach also offers a reusable template for other scientific domains, as the generic DCAT‑AP+ layer can be specialized without breaking interoperability.

In the discussion, the authors acknowledge current limitations, such as incomplete support for datatype unions in LinkML, the need for careful alignment between PROV‑O and other ontologies (BFO, OBI), and the necessity of close collaboration between domain experts and metadata engineers. Future work includes extending datatype union handling, automating mappings to additional domain vocabularies, and developing user‑friendly GUI tools for schema authoring.

Overall, the paper presents a robust, standards‑compliant solution that enhances FAIR data principles for chemistry and catalysis, and it establishes a scalable blueprint for cross‑domain semantic interoperability.