Semantic AI

Traditional AI focuses on syntax – the literal words and symbols in data. Semantic AI adds meaning. By linking content to ontologies, taxonomies and knowledge graphs, it helps machines recognize what concepts represent, how they relate and why they matter.

This approach allows AI to interpret implicit intent rather than just explicit terms. For example, it can understand that “departure times for Glasgow buses” and “Glasgow bus timetables” refer to the same concept, even though the phrasing differs. Similarly, it knows that “rock music” refers to a genre, not a geological object. In practice, Semantic AI transforms how organizations find, manage and connect information. It powers intelligent search, recommendation systems, content classification and personalized user experiences. By bridging linguistic nuance and data structure, Semantic AI brings genuine understanding to digital experiences – turning information into knowledge.

At RWS, Semantic AI is where language meets logic – the layer that connects meaning with structure. It underpins how our Tridion platform enables enterprises to create, manage and deliver intelligent content that adapts to each user and context.

By combining structured content management with semantic technology, Tridion helps organizations unify data, break down silos and improve findability across global ecosystems. Language Weaver and TrainAI extend that understanding further – ensuring that both human and machine communication remain contextually correct and culturally relevant. We see Semantic AI as the foundation for meaningful automation. It ensures that every system – from chatbots to content platforms – interprets language the way people do: with understanding, nuance and intent.