Model Concepts (OWL Ontology)
Extension ontology for modeling ML-enabled system architectures.
Provides element types, relationship types, stakeholders, concerns, and viewpoints
for describing the ML aspects of software systems — training pipelines, model serving,
feature engineering, data lineage, and the integration boundary between ML and non-ML
components.
Motivated by the gap identified in Moin et al. (2023): existing architecture frameworks
lack stakeholders, viewpoints, and model kinds for data scientists, data engineers, and
ML engineers. This extension addresses that gap within the Linked.Archi ecosystem.
https://meta.linked.archi/ml-systems/onto#
Formal Rules (SHACL Shapes)
SHACL shapes for validating ML-enabled system architecture models.
Enforces governance rules: every ML model must have versioning, monitoring,
and dataset lineage; every serving infrastructure must have latency SLAs.
https://meta.linked.archi/ml-systems/shapes#
Concept Classification (SKOS)
Classification of ML-enabled system concepts by lifecycle phase and component role.
https://meta.linked.archi/ml-systems/tax#MLSystemsConceptScheme
