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Query the Semantica knowledge graph using SPARQL, Cypher, keyword search, and structured graph query patterns.
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Basé sur la classification professionnelle 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 | query |
| description | Query the Semantica knowledge graph using SPARQL, Cypher, keyword search, and structured graph query patterns. |
Run graph queries and search. Usage: /semantica:query <mode> [args]
$ARGUMENTS = query mode + query string or filter.
sparql <query>Execute a SPARQL query against the graph.
from semantica.query import QueryEngine
engine = QueryEngine()
results = engine.query_sparql(query)
Return: query bindings as a Markdown table.
cypher <query>Execute a Cypher-like query.
results = engine.query_cypher(query)
Output: node/relationship results and path summaries.
search <keywords> [--filter <type>]Search graph entities by keyword.
results = engine.search(keywords=keywords, filter_type=filter_type)
Return: ranked matches with entity types and relevance scores.