Knowledge graph
Description
Knowledge graphs organize information in a way that reflects how humans understand the world: through connections. Each entity – a person, place, topic or thing – becomes a node in a network, linked by relationships that define context and meaning.
For example, a knowledge graph for "philosophy" might connect to "Plato" and "Aristotle," which both link to "Ancient Greece." This web of relationships helps machines understand not only that these terms are related, but how they’re related. In enterprise environments, knowledge graphs enrich structured content, support intelligent tagging and enable personalization. They form the backbone of Semantic AI – helping systems link concepts across languages, channels and data types.
Example use cases
- Search: Deliver intent-based results rather than keyword matches via semantic search.
- Enrichment: Enrich structured content with contextual relationships and metadata.
- Chatbots: Improve understanding and accuracy of responses through linked knowledge.
- Machine learning: Provide structured, contextualized data for model training.
- Intelligence: Connect data sources to uncover hidden insights and relationships.
Key benefits
RWS perspective
At RWS, knowledge graphs are central to how we connect people, content and context. They power the intelligence behind Tridion – our content management platform – by structuring relationships between information, intent and audience.
By combining Semantic AI, structured content and knowledge graphs, Tridion enables enterprises to create intelligent content ecosystems. This ensures that every piece of information – from a product description to a support article – can be found, understood and reused wherever it’s needed. Through this approach, RWS helps organizations deliver more intuitive experiences, improve search performance and support AI applications that depend on clean, contextual data.