ingest

To ingest data from a CSV file into a knowledge graph, you provide the source to the ingestion workflow, which applies schema mapping and entity linking to populate the graph with structured entities.

Yes, data ingestion supports REST APIs and streaming sources alongside local files and databases, enabling continuous knowledge graph enrichment and incremental updates for downstream reasoning.

Schema mapping and entity linking in data integration align ingested records from multiple sources with the target knowledge graph ontology, ensuring consistent structured graph population and accurate entity relationships.

Scripts are optional for knowledge graph population. The data integration workflow handles schema mapping and entity linking automatically, but scripts can support deterministic tasks when needed.

The best way to track provenance during knowledge graph data ingestion is to route files, databases, or API streams through the ETL pipeline, which natively records provenance tracking alongside schema mapping.

Yes, data ingestion from databases supports incremental knowledge graph updates, applying schema mapping and entity linking to align new records with the existing ontology for consistent graph enrichment.