Two examples of when it is important to provide the right content in the right context and at the right granularity:
- For a support technician who needs to solve a problem on site.
- For an employee who needs further training on a specific topic.
That’s just two – there are many more such needs.
What is usually available: the right content is often very distributed, unstructured and not connected at all. What you may get to solve a problem, for example, is just a list of documents. It doesn’t have to be like that.
Applying knowledge graphs to content makes it possible to create intelligent content graphs that provide content as a service. This approach offers new possibilities for tailoring content to different information needs and making it available in the right way – best suited to the context.
The session focuses on
- How to turn content into an intelligent content graph.
- What role taxonomies and ontologies play in the process.
- How large language models (LLMs) can be integrated into this approach.
Knowledge graphs combined with LLMs are the key to transparent and accurate content delivery. This combination enables the creation of AI solutions that users can trust.
