Composable Trust Infrastructure for Manufacturing Knowledge Graphs: Cross-System Provenance, Temporal Reasoning, and Decision Traceability
arXiv cs.AIen
arXiv:2608.21418v1 Announce Type: new Abstract: Manufacturing knowledge graphs that integrate data from heterogeneous industrial systems face a trust deficit: consumers cannot determine whether queried data is valid, whether it was valid when a decision was made, where it originated, or how it was acted upon. We argue that four trust capabilities -- SHACL validation, PROV-O provenance, domain-aware bi-temporal versioning, and graph-native decision objects -- compose through shared correlation identifiers to produce emergent trust properties that no single capability delivers alone. We present a composable trust infrastructure that integrates these four capabilities into a unified RDF archite
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Forskning
Related AI news
- RIACT: A Responsible AI System for Personalized Study Habit Tracking and Early Burnout Signal Detection in University StudentsarXiv cs.AI · August 25, 2026
- Retrieval-grounded robot program generation and simulation-based correction via Model Context ProtocolarXiv cs.AI · August 25, 2026
- Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation modelsarXiv cs.AI · August 25, 2026
- A Reproducible, License-Aware Distillation Recipe for CPUDeployable Safety ClassificationarXiv cs.AI · August 25, 2026
- Data-Driven Dynamic Algorithm Dispatch with Large Language ModelsarXiv cs.AI · August 25, 2026
- Generate in the Chart, Not on the Boundary: Function-Symbol Grounding for Hard Constraints in LTN-GANsarXiv cs.AI · August 25, 2026