The Seven Cases for Knowledge Graph Integration in a RAG Architecture

Many companies have started to make use of LLMs and have implemented RAG architectures – making it seem as if even the most complicated information management challenges could finally be solved fully automatically with the help of generative AI. But experiments with real data have proved that these “solutions” weren’t good solutions at all. This white paper aims to provide seven cases for knowledge graphs in RAG architecture that not only remedy the issues encountered but provide additional benefits on top.

Though contemporary RAG architectures combine LLMs with vector databases for document search, their use cases are typically limited to less critical processes. Recent trends now point to a fusion of symbolic AI such as knowledge models and graphs with statistical AI such as GenAI. RAG systems will no longer rely solely on vector databases, but also on domain knowledge models that provide additional contextual information about the respective knowledge area, and on graphs that enable efficient access to different knowledge bases within an organization.

The seven cases for knowledge graphs in RAG architecture are as follows:

1. PROVIDE ADDITIONAL CONTEXT FROM KNOWLEDGE MODELS

2. PROVISION OF LINKED FACTS WITH THE HELP OF KNOWLEDGE GRAPHS

3. MAKE USE OF EXPLAINABLE REASONING

4. PERSONALIZATION

5. FUSE STRUCTURED CONTENT WITH KNOWLEDGE MODELS

6. EFFICIENT FILTERING OF RESULTS

7. USER QUERY ASSISTANT

Read more about it in our free white paper, written by Semantic Web Company CEO Andreas Blumauer.