Embeddings encode semantic meaning into numeric vectors, enabling systems to measure conceptual similarity. In a knowledge graph, queries are traditionally limited to explicit relationships—matching nodes by ID or traversing predefined edges. By integrating embeddings, developers can expand these queries to include “soft” matches, such as entities with related descriptions or overlapping topics.
For instance, if a graph stores academic papers as nodes, embeddings of abstracts can be stored in Milvus. A search for “neural retrieval” would not only find exact matches but also semantically related works, like “vector search” or “dense retrieval.” The results are linked back to the graph, where further reasoning can occur—such as finding authors or institutions connected to those papers.
This synergy turns static queries into adaptive, context-aware retrievals. With Milvus or Zilliz providing high-speed vector search and the graph offering structural clarity, developers can craft systems that support both approximate and exact reasoning. The outcome is more comprehensive results without sacrificing precision or explainability.