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Manage ontology schemas, concepts, relationships, and alignments for Semantica knowledge graphs.
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Basado en la clasificación ocupacional SOC
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.
Ingest data from files, databases, APIs, or streams into Semantica knowledge graphs with schema mapping and entity linking.
| name | ontology |
| description | Manage ontology schemas, concepts, relationships, and alignments for Semantica knowledge graphs. |
Manage ontology definitions and validation. Usage: /semantica:ontology <task> [args]
$ARGUMENTS = task + optional ontology item or schema file.
describe <concept>Show ontology concept details.
from semantica.ontology import OntologyManager
manager = OntologyManager()
concept = manager.get_concept(concept_name)
Output: properties, relationships, inherited types, and examples.
validate [--schema <file>]Validate the graph or schema against the ontology.
result = manager.validate_graph(graph=graph, schema_file=schema_file)
Return: validation status, errors, and correction suggestions.