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Ingest data from files, databases, APIs, or streams into Semantica knowledge graphs with schema mapping and entity linking.
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Based on SOC occupation classification
Semantica full-stack knowledge graph skill for context graphs, decision intelligence, explainability, extraction, reasoning, visualization, ontology, provenance, policy, and export workflows.
Analyze cause-and-effect relationships in the Semantica knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.
Explain Semantica reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale.
Detect duplicate entities, duplicate groups, and relationship duplicates in Semantica using fuzzy matching, schema heuristics, and graph similarity.
Export Semantica graphs, results, and provenance to JSON, RDF, Parquet, CSV, GraphML, and other formats.
Track and inspect graph changes, diffs, temporal updates, and the impact of new data on Semantica knowledge graphs.
| name | ingest |
| description | Ingest data from files, databases, APIs, or streams into Semantica knowledge graphs with schema mapping and entity linking. |
Ingest new data into the knowledge graph. Usage: /semantica:ingest <source> [args]
$ARGUMENTS = source type + optional file path, connection string, or dataset identifier.
file <path> [--format json|csv|yaml|xml]Ingest structured data from a local file.
from semantica.ingest import ingest_file
data = ingest_file(file_path=path, method='file', file_format=file_format)
Output: imported node/edge count and ingestion summary.
db <connection> [--query <sql>]Ingest data from a database source.
from semantica.ingest import ingest_database
result = ingest_database(connection_string=conn, query=query)
Return: rows ingested, mapped entities, and warnings.