Traditional graph queries match nodes by IDs or exact attributes. Vector similarity enables approximate matching based on meaning instead of syntax. By embedding descriptive fields of nodes and storing them in Zilliz, a query can retrieve semantically related entities before traversing their explicit relationships.
This approach reduces query blind spots. For example, a search for “autonomous vehicles” can surface “self-driving cars” or “driver-assist systems,” even if the exact terms don’t appear in the graph. Zilliz returns candidate entities ranked by similarity score, and the graph engine uses those nodes as entry points for further reasoning.
Integrating semantic similarity also streamlines recommendation and link prediction. Developers gain a richer context space while retaining graph explainability. Zilliz’s indexing and caching ensure that semantic retrieval remains low-latency even with millions of vectors.