Semantic interoperability (SI) refers to the ability of different AI systems to exchange data and interpret its meaning correctly. By standardising the meanings of words and concepts, which are then structured in a machine-readable format, intelligent systems can use SI to reason over a wider range of datasets, enhancing their usefulness and accuracy.
How is semantic alignment achieved?
The semantic part of SI addresses the problem of ‘different words for the same thing’ that prevents AI systems from uniting their knowledge and drawing meaning from it reliably. It also addresses the problem of the same word meaning different things in different contexts (polysemy), another source of confusion, although this is a much harder challenge for AI.
Semantic alignment therefore requires subject matter experts to agree on the precise meanings of key words within their domain, and how concepts relate to one another, assuming they don’t exist already. These definitions then need to be aligned with other systems that they want to connect with. This stabilises meaning so AI can think consistently.
In large or complex domains such as medical research, reaching consensus on definitions may involve techniques such as the Delphi Method.
How is interoperability achieved?
The interoperability part of SI involves structuring the domain’s knowledge as a high level ontology, and imposing rules and constraints on it, so it becomes machine-readable for AI. These ontological knowledge frameworks are generally built using ontology standards like OWL (Web Ontology Language) and RDF. Standard formats and principles of this sort make it easier for different systems to link up (interoperate) across the global economy, providing the conditions for the semantic web to grow.
Once high level alignment is agreed between different systems, then ontology tools, semantic matching algorithms, and crosswalk standards (like SKOS mappings) can be used to resolve the technical detail, ready for testing. At this point the ontology’s categories – such as ‘customer’ for example – can be populated with actual customer details from the domain, via a knowledge graph.
To ensure that systems stay in semantic alignment, ontology engineers need to perform ongoing monitoring and maintenance. This avoids what is known as semantic drift, where the meanings of words gradually change over time.
How does AI benefit from semantic interoperability?
Semantic interoperability enables AI to reason consistently across wider datasets, helping to avoid misunderstandings and operational mistakes. The greater contextual understanding that flows from SI – such as applying overarching rules and principles to govern the model – enables automation to be entrusted with more sophisticated workflows.
On a more technical level, SI can enable structural adjustments to align data formats and relationships; convert disparate datasets into harmonised structures; and perform verification and gap analysis to identify inconsistencies, or areas that need enrichment.
What is semantic interoperability used for?
In an increasingly data-driven world, SI paves the way for scalable AI agents, cross-domain analytics, application ontologies, and a range of intelligent digital services. Sectors that rely on SI include medical research, advanced manufacturing and, increasingly, localisation initiatives by global brands where the meanings of words need to remain consistent across different languages and cultures.
A 2025 paper explored how ontologies can act as the semantic bridge between AI and healthcare to enhance data interoperability, clinical decision support and precision medicine.
Does semantic interoperability require an engineer?
While a knowledge engineer might be needed for very complex, unstructured domains, new ontology-as-a-service (OaaS) platforms are emerging that make the process of obtaining semantic interoperability easier for non-technical teams. These platforms help with the generation and customisation of controlled vocabularies and metadata. They can also support consensus-building, and help align the model with future developments such as the agentic web, which are going to rely on SI to fulfil their potential.