The ability of systems to exchange data and preserve the meaning of that data across boundaries.
Semantic interoperability is the ability of two or more systems to exchange information and for the receiving system to interpret that information correctly — preserving its intended meaning. It goes beyond syntactic interoperability (agreeing on data formats) and structural interoperability (agreeing on data models) to ensure that the semantic content — the actual meaning of the data — is preserved across system boundaries.
Semantic interoperability has become the central challenge of enterprise AI in 2026. As AI agents operate across organizational boundaries — querying partner APIs, integrating with vendor systems, and consuming public data — the ability to correctly interpret data from external sources is critical. The Open Semantic Interchange (OSI) initiative, the MCP standard, and the proliferation of semantic layer tools are all attempts to solve the semantic interoperability problem at scale.
Semantic interoperability is achieved through shared ontologies (both systems agree on what terms mean), ontology alignment (mapping between different ontologies), semantic annotations (tagging data with references to shared ontologies), and semantic mediation (translating between different semantic representations). Standards like RDF, OWL, SKOS, and JSON-LD provide the technical foundation for semantic interoperability.
A supply chain AI system needs to integrate data from 50 different suppliers, each using their own product catalog format and terminology. Semantic interoperability is achieved by mapping each supplier's product taxonomy to a shared industry ontology (GS1), allowing the AI to correctly identify that 'Widget Type A' from Supplier 1 and 'Component WA-001' from Supplier 2 are the same product — enabling accurate inventory and demand forecasting.