Knowledge Graph vs Vector Database

Graph Database vs Vector Database

Knowledge Graph vs Vector Database

If you are working on a large language model (LLM) project, you might have encountered the problem of hallucination. Hallucination is when the LLM generates text that is fluent and coherent, but not factual or accurate. For example, the LLM might generate a sentence like “The capital of France is Berlin”, which is clearly wrong.

One way to prevent hallucination is to use external knowledge sources, such as databases or knowledge graphs, to provide factual information to the LLM. However, not all knowledge sources are equally effective. In this blog post, I will argue that knowledge graphs are a better solution for LLM hallucination than vector databases.

vector database is a collection of high-dimensional vectors that represent entities or concepts, such as words, phrases, or documents. A vector database can be used to measure the similarity or relatedness between different entities or concepts, based on their vector representations. For example, a vector database can tell you that “Paris” and “France” are more related than “Paris” and “Germany”, based on their vector distances.

knowledge graph is a collection of nodes and edges that represent entities or concepts, and their relationships, such as facts, properties, or categories. A knowledge graph can be used to query or infer factual information about different entities or concepts, based on their node and edge attributes. For example, a knowledge graph can tell you that “Paris” is the capital of “France”, based on their edge label.

 
Knowledge Graph vs Vector Database

Why are knowledge graphs better than vector databases for LLM hallucination?

Here are some reasons:

In conclusion, knowledge graphs are a better solution for LLM hallucination than vector databases, because they can provide more precise, specific, complex, diverse, reasoning and inference information to the LLM. This can help the LLM generate text that is more factual, accurate, relevant, diverse, interesting, logical and consistent.

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