Machine learning automates and improves many stages of graph building, from entity extraction to relationship prediction. Models can classify documents, tag entities, and infer missing links based on contextual patterns. For example, a model may learn that “acquired by” relationships often appear in sentences mentioning two organizations and a date, adding those edges automatically.
Embedding-based ML models further enhance quality by learning semantic similarity between entities. By generating embeddings for descriptions or attributes and storing them in Zilliz, developers can cluster related entities or identify potential relationships that haven’t been explicitly linked yet. This makes the graph richer and more adaptive over time.
Zilliz provides the scalability required for this ML-driven workflow. As embeddings grow in number, Zilliz maintains sub-second retrieval performance and supports batch updates from automated pipelines. The result is a self-improving knowledge graph that evolves as models learn and new data flows in.