@prefix : . @prefix owl: . @prefix rdf: . @prefix xml: . @prefix xsd: . @prefix rdfs: . @base . rdf:type owl:Ontology ; owl:imports ; """Example architecture model for a real-time fraud detection system that demonstrates the ML-Enabled Systems extension. Shows how ML components (models, pipelines, feature stores) integrate with traditional software components (payment service, transaction database, notification service)."""@en ; "Fraud Detection ML-Enabled System — Example Model"@en ; "2026-05-03"^^xsd:date ; "2026-05-03"^^xsd:date ; "fraud" ; "https://model.example.com/fraud-detection#" . ################################################################# # Annotation properties ################################################################# ### http://purl.org/dc/elements/1.1/description rdf:type owl:AnnotationProperty . ### http://purl.org/dc/elements/1.1/title rdf:type owl:AnnotationProperty . ### http://purl.org/dc/terms/created rdf:type owl:AnnotationProperty . ### http://purl.org/dc/terms/modified rdf:type owl:AnnotationProperty . ### http://purl.org/vocab/vann/preferredNamespacePrefix rdf:type owl:AnnotationProperty . ### http://purl.org/vocab/vann/preferredNamespaceUri rdf:type owl:AnnotationProperty . ### http://www.w3.org/2004/02/skos/core#definition rdf:type owl:AnnotationProperty . ### http://www.w3.org/2004/02/skos/core#prefLabel rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#addressesForce rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#hasAlternative rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#hasImportanceMeasure rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#hasIssue rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#hasSelectedOption rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#influencedByForce rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#justification rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#onQualityAttribute rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#problemStatement rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#qasArtifact rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#qasEnvironment rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#qasResponse rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#qasResponseMeasure rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#qasSourceOfStimulus rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#qasStimulus rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/arch-decision#relatedConcept rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/core#rationale rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/core#source rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/core#target rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#consumesData rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#feedsFeatures rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#hasDataLineage rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#hasFramework rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#hasModelArchitecture rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#hasModelVersion rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#hasMonitoringPlan rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#hasPerformanceMetric rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#hasRetrainingSchedule rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#hasServingLatencySLA rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#integratesWith rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#monitors rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#producesDataset rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#producesModel rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#qualifiedIntegratesWith rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#registeredIn rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#serves rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#trainedOn rdf:type owl:AnnotationProperty . ### https://meta.linked.archi/ml-systems/onto#triggersRetraining rdf:type owl:AnnotationProperty . ################################################################# # Datatypes ################################################################# ### http://www.w3.org/2001/XMLSchema#date xsd:date rdf:type rdfs:Datatype . ################################################################# # Classes ################################################################# ### https://meta.linked.archi/arch-decision#BusinessConstraint rdf:type owl:Class . ### https://meta.linked.archi/arch-decision#Decision rdf:type owl:Class . ### https://meta.linked.archi/arch-decision#Issue rdf:type owl:Class . ### https://meta.linked.archi/arch-decision#Option rdf:type owl:Class . ### https://meta.linked.archi/arch-decision#QualityAttributeRequirement rdf:type owl:Class . ### https://meta.linked.archi/core#Element rdf:type owl:Class . ### https://meta.linked.archi/core#Stakeholder rdf:type owl:Class . ### https://meta.linked.archi/ml-systems/onto#DataPipeline rdf:type owl:Class . ### https://meta.linked.archi/ml-systems/onto#Dataset rdf:type owl:Class . ### https://meta.linked.archi/ml-systems/onto#ExperimentTracker rdf:type owl:Class . ### https://meta.linked.archi/ml-systems/onto#FeatureStore rdf:type owl:Class . ### https://meta.linked.archi/ml-systems/onto#IntegratesWith rdf:type owl:Class . ### https://meta.linked.archi/ml-systems/onto#MLModel rdf:type owl:Class . ### https://meta.linked.archi/ml-systems/onto#ModelRegistry rdf:type owl:Class . ### https://meta.linked.archi/ml-systems/onto#MonitoringComponent rdf:type owl:Class . ### https://meta.linked.archi/ml-systems/onto#ServingInfrastructure rdf:type owl:Class . ### https://meta.linked.archi/ml-systems/onto#TrainingPipeline rdf:type owl:Class . ################################################################# # Individuals ################################################################# ### https://model.example.com/fraud-detection#ADR001-ModelChoice :ADR001-ModelChoice rdf:type owl:NamedIndividual , ; "ADR-001: Use XGBoost for Fraud Detection"@en ; :Option-LSTM , :Option-RandomForest ; :Issue-ModelSelection ; :Option-XGBoost ; :Force-Explainability , :Force-InfraCost , :Force-Latency ; """XGBoost chosen over deep learning (LSTM) because: 1. Comparable accuracy on tabular transaction data (AUC 0.96 vs 0.97) 2. 10x faster inference (5ms vs 50ms) — critical for real-time scoring 3. Better explainability — SHAP values directly available 4. Simpler serving infrastructure — no GPU required"""@en ; :FraudDetectionModel . ### https://model.example.com/fraud-detection#CaseManagementUI :CaseManagementUI rdf:type owl:NamedIndividual , ; "Web application used by fraud analysts to review flagged transactions."@en ; "Fraud Case Management UI"@en . ### https://model.example.com/fraud-detection#ComplianceOfficer :ComplianceOfficer rdf:type owl:NamedIndividual , ; "Responsible for regulatory compliance of the fraud detection system (PSD2, GDPR)."@en ; "Compliance Officer"@en . ### https://model.example.com/fraud-detection#FeatureDataset :FeatureDataset rdf:type owl:NamedIndividual , ; "Feature-engineered dataset derived from raw transactions — velocity features, aggregates, embeddings."@en ; "Engineered Feature Dataset v2.3"@en ; :TransactionDataset . ### https://model.example.com/fraud-detection#Force-Explainability :Force-Explainability rdf:type owl:NamedIndividual , ; "PSD2 requires that declined transactions can be explained to customers."@en ; "Model Explainability for PSD2 Compliance"@en ; "critical" ; . ### https://model.example.com/fraud-detection#Force-InfraCost :Force-InfraCost rdf:type owl:NamedIndividual , ; "Current Kubernetes cluster has no GPU nodes. Adding GPU support requires 3-month procurement cycle."@en ; "No GPU Infrastructure"@en ; "high" . ### https://model.example.com/fraud-detection#Force-Latency :Force-Latency rdf:type owl:NamedIndividual , ; "Real-time Latency Requirement"@en ; "critical" ; "Fraud score returned"@en ; "p99 < 50ms inference latency"@en ; "Transaction authorization request"@en . ### https://model.example.com/fraud-detection#Force-SequentialPatterns :Force-SequentialPatterns rdf:type owl:NamedIndividual , ; "Ability to detect fraud patterns across sequences of transactions."@en ; "Sequential Transaction Pattern Detection"@en ; "medium" . ### https://model.example.com/fraud-detection#FraudDataScientist :FraudDataScientist rdf:type owl:NamedIndividual , ; "Data scientists responsible for developing and improving fraud detection models."@en ; "Fraud Analytics Team — Data Scientists"@en . ### https://model.example.com/fraud-detection#FraudDetectionModel :FraudDetectionModel rdf:type owl:NamedIndividual , ; """XGBoost gradient-boosted ensemble for real-time transaction fraud scoring. Outputs a fraud probability score (0.0–1.0) for each transaction. Transactions scoring above 0.85 are blocked; 0.60–0.85 are flagged for manual review."""@en ; "Fraud Detection Model v3.1"@en ; "XGBoost 2.0 + scikit-learn 1.4" ; "XGBoost ensemble, 500 trees, max_depth=8, learning_rate=0.05" ; "3.1.0" ; :FraudModelMonitor ; "precision=0.92, recall=0.87, F1=0.89, AUC-ROC=0.96" ; "Weekly, plus on-demand when drift detected" ; :FraudModelRegistry ; :FeatureDataset . ### https://model.example.com/fraud-detection#FraudExperimentTracker :FraudExperimentTracker rdf:type owl:NamedIndividual , ; "MLflow instance tracking all fraud model experiments — hyperparameters, metrics, artifacts."@en ; "MLflow Experiment Tracker"@en . ### https://model.example.com/fraud-detection#FraudFeatureStore :FraudFeatureStore rdf:type owl:NamedIndividual , ; """Feast-based feature store providing consistent features for both training and real-time serving. Features include transaction velocity (count/sum over 1h/24h/7d windows), merchant risk scores, and device fingerprint embeddings."""@en ; "Fraud Feature Store"@en ; :FraudModelServer , :FraudTrainingPipeline . ### https://model.example.com/fraud-detection#FraudModelMonitor :FraudModelMonitor rdf:type owl:NamedIndividual , ; """Evidently AI-based monitoring component tracking: - Prediction distribution (score histogram drift) - Feature drift (PSI on top 20 features) - Model performance (precision/recall on labeled feedback) - Serving latency and error rates"""@en ; "Fraud Model Monitor"@en ; :NotificationService ; :FraudDetectionModel , :FraudModelServer ; :FraudTrainingPipeline . ### https://model.example.com/fraud-detection#FraudModelRegistry :FraudModelRegistry rdf:type owl:NamedIndividual , ; "MLflow Model Registry tracking all fraud model versions, their approval status, and deployment targets."@en ; "Fraud Model Registry"@en . ### https://model.example.com/fraud-detection#FraudModelServer :FraudModelServer rdf:type owl:NamedIndividual , ; """Seldon Core deployment serving the fraud detection model via gRPC. Receives transaction features, returns fraud probability score. Deployed on Kubernetes with autoscaling (2–20 replicas)."""@en ; "Fraud Scoring Service"@en ; "p99 < 50ms" ; :CaseManagementUI , :PaymentService ; :FraudDetectionModel . ### https://model.example.com/fraud-detection#FraudTrainingPipeline :FraudTrainingPipeline rdf:type owl:NamedIndividual , ; """Kubeflow pipeline that trains the fraud detection model: 1. Pull features from feature store 2. Split train/validation/test 3. Train XGBoost ensemble 4. Evaluate on holdout set 5. Register model if metrics pass threshold"""@en ; "Fraud Model Training Pipeline"@en ; :FraudDetectionModel . ### https://model.example.com/fraud-detection#Issue-ModelSelection :Issue-ModelSelection rdf:type owl:NamedIndividual , ; "Which ML algorithm for real-time fraud scoring?"@en ; "Need to select an ML algorithm that balances detection accuracy, inference latency, and explainability for real-time transaction fraud scoring."@en . ### https://model.example.com/fraud-detection#NotificationService :NotificationService rdf:type owl:NamedIndividual , ; "Service that sends fraud alerts to customers and compliance team."@en ; "Notification Service"@en . ### https://model.example.com/fraud-detection#Option-LSTM :Option-LSTM rdf:type owl:NamedIndividual , ; "LSTM Recurrent Neural Network"@en ; :Force-SequentialPatterns . ### https://model.example.com/fraud-detection#Option-RandomForest :Option-RandomForest rdf:type owl:NamedIndividual , ; "Random Forest"@en ; :Force-Latency . ### https://model.example.com/fraud-detection#Option-XGBoost :Option-XGBoost rdf:type owl:NamedIndividual , ; "XGBoost Gradient Boosted Ensemble"@en ; :Force-Explainability , :Force-Latency . ### https://model.example.com/fraud-detection#PaymentService :PaymentService rdf:type owl:NamedIndividual , ; "Core payment processing service that handles transaction authorization."@en ; "Payment Processing Service"@en ; :integration-payment-scoring . ### https://model.example.com/fraud-detection#PaymentsSoftwareEngineer :PaymentsSoftwareEngineer rdf:type owl:NamedIndividual , ; "Software engineers responsible for the payment processing service."@en ; "Payments Team — Software Engineers"@en . ### https://model.example.com/fraud-detection#PlatformDataEngineer :PlatformDataEngineer rdf:type owl:NamedIndividual , ; "Data engineers responsible for ML infrastructure, pipelines, and model serving."@en ; "Platform Team — Data Engineers"@en . ### https://model.example.com/fraud-detection#QAS-DriftDetection :QAS-DriftDetection rdf:type owl:NamedIndividual , ; "Drift Detection Response Time"@en ; ; :FraudDetectionModel ; "Normal production operation"@en ; "Alert raised, retraining pipeline triggered automatically"@en ; "Drift detected within 4 hours, retrained model deployed within 24 hours"@en ; "Changing fraud patterns (new attack vectors)"@en ; "Feature distribution shift exceeds PSI threshold of 0.2"@en . ### https://model.example.com/fraud-detection#QAS-FairnessAudit :QAS-FairnessAudit rdf:type owl:NamedIndividual , ; "Fraud Model Fairness Audit"@en ; ; :FraudDetectionModel ; "Audit environment with production model snapshot"@en ; "Fairness report generated showing false positive rates by demographic group"@en ; "Max disparity in false positive rate across groups < 5%"@en ; "Quarterly compliance audit"@en ; "Audit request for demographic parity analysis"@en . ### https://model.example.com/fraud-detection#TransactionDB :TransactionDB rdf:type owl:NamedIndividual , ; "PostgreSQL database storing all payment transactions."@en ; "Transaction Database"@en . ### https://model.example.com/fraud-detection#TransactionDataset :TransactionDataset rdf:type owl:NamedIndividual , ; "Labeled transaction dataset with fraud/non-fraud labels, covering 18 months of history."@en ; "Transaction Training Dataset v2.3"@en ; :TransactionDB . ### https://model.example.com/fraud-detection#TransactionETL :TransactionETL rdf:type owl:NamedIndividual , ; "Airflow DAG that extracts transactions from the DB, transforms them, and loads into the feature store."@en ; "Transaction ETL Pipeline"@en ; :TransactionDB ; :TransactionDataset . ### https://model.example.com/fraud-detection#integration-payment-scoring :integration-payment-scoring rdf:type owl:NamedIndividual , ; """Payment service calls fraud scoring service via gRPC for every transaction. Contract: send TransactionFeatures proto, receive FraudScore proto. Latency budget: 50ms (within the 200ms total authorization window). Fallback: if scoring service is unavailable, approve transaction and flag for async review (fail-open policy agreed with risk team)."""@en ; "Fraud Scoring Integration"@en ; "Fail-open chosen over fail-closed to avoid blocking legitimate transactions during outages."@en ; :FraudModelServer ; :PaymentService . ### Generated by the OWL API (version 5.1.18) https://github.com/owlcs/owlapi/