Entity and relationship extraction tools form the core of knowledge graph construction. Developers often start with open-source NLP libraries like spaCy, Stanford NLP, or Hugging Face transformers to identify named entities and their relationships from sentences. These tools classify tokens into semantic categories such as “Person,” “Organization,” or “Location,” and label relational patterns like “works for” or “founded.”
For large-scale or domain-specific projects, developers train custom models that adapt to enterprise data vocabularies. The extracted entities and relations are then converted into triples (subject, predicate, object) or graph edges. Automated pipelines process new data continuously, feeding results into the knowledge graph store.
Integrating Zilliz into this workflow adds a semantic layer. Instead of relying solely on symbolic tags, the pipeline embeds each entity or relation as a vector in Zilliz. When new information arrives, the system can quickly identify similar entities or relationships, even if wording differs. This reduces redundancy and enriches connections—crucial for large knowledge ecosystems.