D7.1 A Framework for Data, Domain, and Process Modelling in Cross-Domain Harmonization

A motivation modelling layer, grounded in the ArchiMate Motivation extension, provides the strategic and semantic context within which all three instruments operate. A key component of the work is the development of a data cartography, supported by WP9 (Task 9.1), which maps the available data resources and their relationships. This cartography extends beyond traditional data models by incorporating metadata that describe the context of the data, including data sources, ownership, temporal and spatial granularity, quality indicators, and provenance.

A dual-layer provenance model (combining a DCAT-AP and PROV-O conceptual representation with an operational OpenLineage lineage capture mechanism) supports traceability and reproducibility across the platform's data pipelines. The conceptual modelling approach addresses the semantic dimension of the platform: how the key concepts used across five distinct contributing domains are defined, related, and made explicit to support coherent integration.

A purpose-built, lightweight notation language (structured around directed relationships, typed property and derivation relationships, and explicit concept definitions) enables domain experts to construct comparable concept maps without requiring formal ontology engineering skills. A systematic cross-domain semantic analysis, currently under way, will derive a shared platform vocabulary from the comparison and reconciliation of these maps.

The functional modelling methodology operationalizes a model-based engineering approach through a standardized functions template. This template captures, for each platform component, its purpose, capabilities, valid operating conditions, modelling formalism, algorithms, execution requirements, and interface specification. The template is designed to be interpretable by both human collaborators and AI agents, providing the structured knowledge base required to support automated composability assessment, semantic interoperability checking, and explainability of composed results.

The template has been distributed to and completed by contributing teams from WP1 to WP4, with an initial cross-analysis revealing semantic inconsistencies across domains that confirm thecore motivation of the methodology. The deliverable concludes that the three modelling instruments are most valuable when developed simultaneously and in relation to one another. The data cartography grounds the functional model in concrete data assets; the conceptual model assigns unambiguous meaning to the concepts that circulate across the platform; and the functional model captures the operational identity of each component in a form consistent with that shared vocabulary.

This layered knowledge structure constitutes thefoundational element of the Noetic Layer of the D2ET platform, enabling coherent integration of data, models, and analytical services across the project ecosystem.