Knowledge graphs and vector search both help AI systems retrieve relevant information, but they represent genuinely different, complementary approaches to organizing and querying knowledge.
Knowledge Graphs: Explicit Relationships
(Marie Curie) --[discovered]--> (Radium) (Marie Curie) --[won]--> (Nobel Prize in Physics, 1903) (Marie Curie) --[won]--> (Nobel Prize in Chemistry, 1911) (Radium) --[is a]--> (Chemical Element)
Knowledge graphs represent information as explicit, structured entities and relationships — genuinely precise, supporting exact, multi-hop logical queries (“find all Nobel laureates who discovered a chemical element”), but requiring real, deliberate effort to construct and continuously maintain as new information emerges.
Vector Search: Semantic Similarity
Vector search instead represents information as embeddings and finds content based on semantic similarity — genuinely fast and flexible to build from raw, unstructured text with minimal manual curation required, but it can’t express precise, exact logical relationships or multi-hop reasoning the way a genuine knowledge graph structurally can.
Why Combining Both Is Increasingly Common
Modern retrieval systems increasingly combine both — using a knowledge graph for precise, structured facts and genuine multi-hop relational queries, and vector search for broader, fuzzier semantic matching across the surrounding unstructured text — since each approach genuinely covers a real, distinct gap the other one structurally can’t.