ZTE's O-R-R-A ontology framework and 璇玑 DataAgent address semantic interoperability gaps for AI agents through incremental ontology engineering and six runtime components, achieving 95% metric unification and minute-level risk identification across 10+ systems.
Technically integrated data fields with identical names can represent different business events, leading to misleading analytics unless semantic metadata aligns the meaning behind each value.
The article explains why most factories cannot implement the 40 official smart manufacturing scenarios, identifying semantic fragmentation across MES, ERP, PLC, and other systems as the core blocker, and shows how OntoL's ontology-based unified semantic layer enables incremental, pain-point-first deployment without ripping out existing IT.
This article argues that data sharing initiatives often fail not due to access permissions but because identical metric names mask divergent definitions, statistical scopes, and processing logic, a problem amplified in AI-driven workflows where semantic context must be explicitly preserved for reliable automated decisions.
