Semantic search extends a knowledge graph’s capabilities beyond keyword matching by interpreting meaning and context. To implement it, developers first embed textual data—like node labels or descriptions—into vectors using a transformer or embedding model. These vectors are stored in a vector database such as Milvus. When a query arrives, it is also embedded and compared against the stored vectors to find semantically similar entities.
The results from Milvus are mapped back to the graph layer. Developers can then traverse the graph to gather connected information, relationships, or attributes. For example, a query about “AI hardware companies” can surface entities like “GPU manufacturers” or “chip design startups,” even if those keywords weren’t explicitly stored in the graph. This fusion of similarity and structure allows for intuitive discovery.
By combining vector retrieval with graph traversal, applications gain both flexibility and transparency. Zilliz provides managed vector infrastructure that scales automatically, allowing semantic enrichment to be applied even to massive knowledge graphs without additional operational overhead.