Knowledge Graph Databases vs Vector Databases

In RAG solutions typically two approaches exist, knowledge graphs and vector databases. While both are used for representing and retrieving information, they differ in their underlying data structures and the way they handle information. The key differences between these two approaches are shown in the following table:


Feature	Knowledge Graphs	Vector Databases
Data Representation	Entities (nodes) and relationships (edges) between entities, forming a graph structure.	High-dimensional vectors, each representing a piece of information (e.g., document, sentence).
Retrieval Mechanisms	Traversing the graph structure and following relationships between entities. Enables inference and derivation of new knowledge.	Vector similarity based on a similarity metric (e.g., cosine similarity). Returns most similar vectors and associated information.
Interpretability	Human-interpretable representation of knowledge. Graph structure and labeled relationships clarify entity connections.	Less interpretable to humans due to high-dimensional numerical representations. Challenging to directly understand relationships or reasoning behind retrieved information.
Knowledge Integration	Facilitates integration by representing entities and relationships in a unified graph structure. Seamless integration if entities and relationships are mapped properly.	More challenging. Requires techniques like vector space alignment or ensemble methods to combine information. Ensuring vector compatibility can be non-trivial.
Inferential Reasoning	Enables inferential reasoning by traversing the graph structure and leveraging relationships between entities. Uncovers implicit connections and derives new insights.	More limited. Relies on vector similarity and may miss implicit relationships or inferences. Can identify similar information but not complex relationships from knowledge graphs.