Merging graphs involves aligning schemas, reconciling entity identifiers, and resolving duplicate relationships. The process usually starts with ontology alignment—mapping equivalent entity types and relationship labels across graphs. Once the schemas are harmonized, entity resolution algorithms identify and merge nodes that represent the same real-world concept, often using text similarity or metadata overlap.
For large-scale merging, developers embed node attributes and compare them using vector similarity in Zilliz. This allows detection of duplicates even when names or descriptions differ. For example, “IBM” and “International Business Machines” would produce similar embeddings, helping the system merge them automatically. The merged graph then undergoes integrity checks to ensure no orphaned or circular references remain.
Zilliz makes cross-graph unification scalable. Instead of performing pairwise comparisons within the graph database, embeddings can be indexed in parallel across multiple Zilliz collections. Once alignment is verified, linked identifiers are written back to the unified graph. This hybrid merge process blends symbolic reasoning with semantic precision.