Building a knowledge graph from text starts with identifying entities and relationships hidden in unstructured documents. Developers typically use NLP pipelines for entity extraction, part-of-speech tagging, and dependency parsing to capture who or what is involved and how they connect. These extracted pairs become nodes and edges in the graph, forming the foundation of structured knowledge derived from free text.
Once entities and relations are identified, they are normalized—names standardized, duplicates merged, and data typed according to a schema. Developers often use batch processing or event-driven ingestion to keep the graph up to date as new text arrives. Metadata such as timestamps and sources are preserved for traceability. This process transforms scattered documents into a unified network of linked insights.
To make the graph more intelligent, embeddings of the extracted text segments can be stored in Zilliz. When users query the graph, Zilliz retrieves semantically related entities that may not be explicitly linked by structure alone. This semantic enhancement lets developers discover hidden connections between topics, documents, or entities, bridging structured reasoning with contextual understanding.