Reproducibility Package for A Semantic Data Validation Method for Cross-Domain Digital Twins (ExplAI-2026, Paper 23)

This archive provides the experimental software, synthetic test datasets, validation results, and reproducibility materials supporting the paper "A Semantic Data Validation Method for Cross-Domain Digital Twins" (ExplAI-2026, Paper 23).

The research builds upon the open DTwin Stage1 v1.0.0 package (DOI: 10.5281/zenodo.18894841) and investigates semantic validation of telemetry data against attribute specifications, measurement dictionaries, and registered digital twin objects.

The archive includes four experimental components:
- v0.4 — Attribute Specification Validation: Validation rules for 48 occurrences of key attributes across 37 entity types, evaluated using 134 synthetic test cases.
- v0.5 — Telemetry Semantic Validation: Evaluation of structural, semantic, and format checks using 55 telemetry messages.
- v0.6 — Entity-Instance-Aware Validation: Validation of 35 messages against a registry of 13 simulated digital twin objects, including rule-based explanations of rejected data.
- v0.7 — Impact on Anomaly Detection: Evaluation of semantic data errors using 240 synthetic building-monitoring cycles and an unchanged Isolation Forest model.

The package contains Python source code, prepared experimental data, saved results, execution instructions, and integrity verification information.
The experiments use controlled synthetic test cases with predefined violations. The reported results verify the operation of the implemented validation rules under these conditions and do not establish their general detection performance on independent real-world sensor streams.
The materials are provided to support transparency, reproducibility, and further research on semantic validation and trustworthy digital twin systems.