Editorial: A Little Semantics Goes a Long Way. It’s Time to Go Further: Lessons from Three Supply Chain Use Cases

Across manufacturing, water intelligence, and transportation supply chains, a consistent pattern emerges: data and tools are abundant, yet systems fail to interoperate effectively. Existing approaches rely on “little semantics”—schemas, labels, and metadata—that enable data exchange but not robust reasoning. We argue that this limitation reflects a deeper bottleneck: the absence of shared, interoperable representations of meaning—where key characteristics such as capabilities, water availability, or freight transportation rates cannot be consistently interpreted because the conditions that define them are not explicitly represented. Through three domain case studies, we show how this gap constrains integration and coordination. We then outline initial steps toward richer semantic representations that support more reliable interoperability and decision-making, suggesting a pragmatic evolution of the Semantic Web—one that builds on the strengths of little semantics while enabling deeper semantics where they matter.