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

Philip Stroemert, Hendrik Borgelt, David Linke, Mark Doerr, Bhavin Katabathuni, Oliver Koepler, Norbert Kockmann

TL;DR

The paper presents DCAT-AP+ as a provenance-centered upper extension to the DCAT-AP metadata framework and ChemDCAT-AP as a chemistry-domain specialization built with LinkML. It describes porting DCAT-AP to LinkML, implementing a Provenance Core Layer and Domain-Specific Layer, and validating with the MIChI standard for NMR (MARGARITAS) and the NFDI4Chem Search Service integration. The approach preserves compatibility with DCAT-AP while providing rich, domain-specific metadata (e.g., InChIKey, SMILES, reaction context) and supports automated data validation and format conversions. The results enable the creation of a Chemistry Knowledge Graph (ChemKG) and broader interoperability across research infrastructures such as EOSC via schemas, registrations, and crosswalks.

Abstract

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.

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

TL;DR

The paper presents DCAT-AP+ as a provenance-centered upper extension to the DCAT-AP metadata framework and ChemDCAT-AP as a chemistry-domain specialization built with LinkML. It describes porting DCAT-AP to LinkML, implementing a Provenance Core Layer and Domain-Specific Layer, and validating with the MIChI standard for NMR (MARGARITAS) and the NFDI4Chem Search Service integration. The approach preserves compatibility with DCAT-AP while providing rich, domain-specific metadata (e.g., InChIKey, SMILES, reaction context) and supports automated data validation and format conversions. The results enable the creation of a Chemistry Knowledge Graph (ChemKG) and broader interoperability across research infrastructures such as EOSC via schemas, registrations, and crosswalks.

Abstract

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.
Paper Structure (19 sections, 1 figure)

This paper contains 19 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Background
  3. The Need for Domain-Specific Metadata
  4. DCAT-AP: An Ontology-Aligned Metadata Schema
  5. Existing DCAT-AP Extensions
  6. Alternatives to DCAT-AP
  7. The LinkML Framework: A Foundational Choice
  8. Methods
  9. Modeling Framework and Implementation
  10. Porting DCAT-AP to LinkML
  11. A Two-Layered Modular Design
  12. The Provenance Core Layer
  13. The Domain-Specific Layer
  14. Validation and Application
  15. Results
  16. ...and 4 more sections

Figures (1)

  • Figure 1: This visualization depicts how we extended the DCAT-AP via the prov:Activity class, to be able to provide additional domain-specific metadata for the entities or activities that are the main subject of a dcat:Dataset, as well as for the instruments and plan that were used within the creation of a dataset and the surrounding in which this activity took place. Also depicted are the mappings we used to ground this LinkML schema within the ontological commitment of PROV-O, DCTerms and QUDT.QUDT