Establishing Trust in Industrial Knowledge Graphs

The paper addresses the critical challenge of maintaining data integrity and decision transparency within complex manufacturing environments. As industrial systems increasingly rely on knowledge graphs to synthesize data from disparate sources, the lack of standardized provenance and temporal tracking creates significant barriers to operational trust. The proposed 'Composable Trust Infrastructure' provides a modular architecture designed to verify the lineage of data points across heterogeneous systems, ensuring that AI-driven decisions are both explainable and auditable.

Core Components of the Trust Framework

To achieve reliable decision traceability, the framework focuses on three primary pillars:

• Cross-System Provenance: By implementing a unified metadata layer, the system tracks the origin and transformation history of data as it moves between ERP, MES, and IoT sensors. This prevents 'black box' scenarios where the source of a faulty manufacturing parameter remains unknown.
• Temporal Reasoning: The architecture incorporates time-aware graph structures that allow for the reconstruction of system states at any specific point in history. This is essential for root-cause analysis, enabling engineers to correlate past machine states with current performance anomalies.
• Decision Traceability: The framework maps automated decisions back to the specific data inputs and logic models used at the time of execution. By creating a verifiable audit trail, the system ensures that AI recommendations in high-stakes manufacturing environments meet safety and compliance standards.

Practical Implementation and Trade-offs

The authors emphasize that building this infrastructure requires a balance between granularity and system performance. While high-fidelity provenance tracking provides maximum transparency, it introduces overhead in data storage and query latency. The paper suggests a tiered approach where critical process parameters receive full temporal logging, while auxiliary data is handled with lighter-weight metadata schemas. This modularity allows manufacturers to scale their trust infrastructure according to the criticality of the specific production line or process.