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Trace data lineage, source attribution, audit trails, and provenance assertions in Semantica graphs.
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基于 SOC 职业分类
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 | provenance |
| description | Trace data lineage, source attribution, audit trails, and provenance assertions in Semantica graphs. |
Inspect provenance metadata. Usage: /semantica:provenance <task> [args]
$ARGUMENTS = task + optional node, edge, or time range.
trace <node_id> [--depth N]Trace the provenance of a node or fact.
from semantica.provenance import ProvenanceTracer
tracer = ProvenanceTracer()
trace = tracer.trace_node(node_id=node_id, depth=depth)
Output: source chain, authors, timestamps, and validation status.
audit [--since <ts>] [--actor <id>]View audit logs for graph changes.
audit_log = tracer.get_audit_log(since=since, actor=actor)
Return: change events, actor, affected objects, and action details.