// Explain Semantica reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale.
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.
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.
Track and inspect graph changes, diffs, temporal updates, and the impact of new data on Semantica knowledge graphs.
| name | explain |
| description | Explain Semantica reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale. |
Produce explanations for decisions, rules, and graph analytics. Usage: /semantica:explain <target> [args]
$ARGUMENTS = explanation target + optional detail level.
decision <decision_id> [--detail <level>]Explain why a decision was reached.
from semantica.reasoning.explanation_generator import ExplanationGenerator
# For decision explainability in Semantica contexts:
decision_trace = ctx.trace_decision_explainability(decision_id=decision_id)
# For reasoning/proof explanations:
generator = ExplanationGenerator(detail_level=detail)
explanation = generator.generate_explanation(reasoning_result)
Output: decision factors, rule traces, confidence, and suggested next steps.
graph <node_id> [--path N]Explain graph relationships and why a node is connected.
# Use AgentContext explainability + causal tracing for graph-connected decisions
graph_explanation = ctx.trace_decision_explainability(decision_id=node_id)
upstream = ctx.get_causal_chain(decision_id=node_id, direction="upstream", max_depth=depth)
downstream = ctx.get_causal_chain(decision_id=node_id, direction="downstream", max_depth=depth)
Return: cause/effect chains, supporting evidence, and relevant metadata.