The Seven Cases for Knowledge Graph Integration in a RAG Architecture

Many companies have started to utilize LLMs and implement RAG architectures, making it seem as though even the most complex information management challenges can be fully automated with the help of generative AI. However, experiments with real data have shown that these “solutions” weren’t effective at all. This white paper aims to provide seven cases for knowledge graphs in RAG architecture that not only remedy the issues encountered but also 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 Andreas Blumauer, SVP of Growth at Graphwise