Generative AI can only answer questions or complete tasks based on what it's been trained on - unless it's given access to external knowledge. The "technology du jour" for providing that external knowledge is the embeddings or vector database, but anyone who knows anything about semantic knowledge graphs knows that a knowledge graph will give much better results.
In the last few days GPT-4 has been credited with "sparks of artificial general intelligence" so I thought I'd ask it which it would prefer. Here's what GPT-4 had to say...
Steve's Question
You're given two stores of external information to choose from, a semantic knowledge graph or a vector database. Think about the steps involved in completing a question or task you (as an LLM) are given, which of these two store types would be more likely to give you the text you need?
GPT-4's Response
As an LLM, I would choose a semantic knowledge graph for completing a question or task.
A semantic knowledge graph represents relationships between entities and concepts in a more structured manner, making it easier to identify the relevant information for a specific question or task. On the other hand, a vector database represents information as numerical vectors in a high-dimensional space, which might be more difficult to interpret and extract the required text directly.
The structured nature of a semantic knowledge graph would allow me to easily navigate and find the information needed to complete the given task.
Five Examples from GPT-4
I asked GPT-4 for examples illustrating why it would make this choice:
- "What are the main causes of air pollution?" A semantic knowledge graph would have structured relationships between entities and concepts, making it easier to identify the causes of air pollution.
- Product recommendations - navigating relationships between products, categories, and user preferences
- Medical diagnosis support - following relationships between symptoms, conditions, and treatments
- Legal research - tracing connections between cases, statutes, and precedents
- Supply chain optimization - understanding relationships between suppliers, products, and logistics
From the Horse's Mouth
So there it is - from the horse's mouth. Large Language Models (LLMs) need access to external knowledge and data if they're to be generally useful and the semantic knowledge graph is the structure of choice.
If you're a developer or provider of semantic graph database applications there will probably never be another opportunity like this one.
Stephen Young is CEO of FactNexus and a knowledge graph and complex data specialist.