🤖 AI Summary
This study addresses the challenge of achieving semantic interoperability across interdisciplinary research data, which is hindered by heterogeneous metadata schemas and ontologies. To overcome this, the authors propose DCAT-AP+, an extensible generic application profile that supports domain specialization through an upper contextual linking layer. Building on LinkML, they develop ChemDCAT-AP, a chemistry-specific sub-profile that enables unified modeling of research data and its provenance. The framework maintains compatibility with the original DCAT-AP while supporting schema inheritance, sub-profile generation, type alignment, and format transformation. Validation in the domains of chemistry and catalysis demonstrates, for the first time, that ChemDCAT-AP effectively facilitates semantic interoperability and data integration across closely related scientific disciplines.
📝 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.