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Detect duplicate entities, duplicate groups, and relationship duplicates in VeritasReason using fuzzy matching, schema heuristics, and graph similarity.
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基于 SOC 职业分类
VeritasReason 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 VeritasReason knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.
Track and inspect graph changes, diffs, temporal updates, and the impact of new data on VeritasReason knowledge graphs.
Full decision lifecycle in VeritasReason � record, query, find precedents (hybrid/advanced), analyze influence, explain, insights dashboard, list, and record exceptions. Uses AgentContext, ContextGraph, DecisionQuery, CausalChainAnalyzer, DecisionRecorder.
Generate, inspect, and use node/text embeddings in VeritasReason — compute Node2Vec embeddings, find similar nodes, score link predictions, batch similarity, and pairwise similarity. Uses NodeEmbedder, SimilarityCalculator, LinkPredictor, and AgentContext. Sub-commands: compute, similar, similarity, predict-link, top-links, batch, pairwise.
Explain VeritasReason reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale.
| name | deduplicate |
| description | Detect duplicate entities, duplicate groups, and relationship duplicates in VeritasReason using fuzzy matching, schema heuristics, and graph similarity. |
Remove duplicates from the knowledge graph. Usage: /veritasreason:deduplicate <strategy> [args]
$ARGUMENTS = deduplication strategy + optional entity or threshold.
entities [--threshold <score>] [--field <name>]Detect duplicate entities and group them by similarity.
from veritasreason.deduplication import DuplicateDetector
finder = DuplicateDetector()
candidates = finder.detect_duplicates(entities, threshold=threshold)
groups = finder.detect_duplicate_groups(entities, threshold=threshold)
Output: duplicate candidate list, duplicate groups, and representative merge recommendations.
relations [--similarity <score>]Detect duplicate relationships and normalize edge representations.
from veritasreason.deduplication import DuplicateDetector
finder = DuplicateDetector()
relations = finder.detect_duplicates(relation_list, threshold=similarity)
Result: duplicate relation candidates, normalized relationship groups, and cleanup summary.