Ying and Yang

LLMs like ChatGPT have taken the world by storm, but for enterprises, they are only half of the equation. Knowledge Graphs (KGs) are the other half, providing the reliability and structured understanding that LLMs lack.

🔵 Transformers - Continuous Knowledge:
LLMs capture the fuzzy, probabilistic nature of relationships between concepts, allowing us to navigate the semantic landscape fluidly, gradually shifting from one concept to the next in a continuous flow. However, this fluidity is both a blessing and a curse. Continuous knowledge representations can be unreliable, leading to hallucinations and ad-libbing, which is problematic for business.

🔵 Knowledge Graphs - Discrete Knowledge:
Knowledge Graphs, on the other hand, offer a discrete, trustworthy counterpart to LLMs. They represent data as nodes and edges, explicitly defining relationships in a way that ensures logical consistency. While KGs are among the most expressive formal data structures available, they have their own challenges. They can be rigid and far less dynamic and flexible than their statistical counterparts.

🔵 KGs + LLMs - Continuous and Discrete Knowledge Combined:
The magic happens when these two forms of knowledge representation come together. KGs and LLMs, when combined, create a powerful tool capable of safeguarding and exploring organisational data. This hybrid approach enables organisations to thrive in an AI-driven world.

In its fullest realisation, this KG + LLM approach should function like a protective membrane, providing a unified semantic layer that encapsulates proprietary information safely while still leveraging the power of AI. Simply put, it’s a survival strategy for organisations entering the age of AI.


⭕ Vectors & Graphs: https://www.knowledge-graph-guys.com/blog/vectors-amp-graphs
⭕ Semantic Layer: https://www.knowledge-graph-guys.com/blog/the-semantic-layer