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KaifAhmad1
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- visualization.md: GraphVisualizer → KGVisualizer; fix method names (visualize_network, visualize_network_evolution, visualize_snapshot_comparison, visualize_temporal_patterns, visualize_2d_projection); remove DistanceVisualizer tab; fix start_explorer() reference - kg.md: remove TemporalKnowledgeGraph and DistanceCalculator (don't exist); replace with TemporalGraphQuery and ConnectivityAnalyzer; fix query_at_time() signature - ontology.md: remove OntologyManager, SKOSVocabulary, OntologyAligner, OntologyDiff, OntologyMigrator (none exist); fix SHACLValidator → OntologyValidator; fix OWLExporter → OWLGenerator.export_owl(); fix start_explorer() reference - evals.md: replace entire file with coming-soon notice (module is a stub, __all__ = []) - embeddings.md: fix EmbeddingGenerator constructor (takes config dict not model=); generate() → generate_embeddings(); similarity() → compare_embeddings() - ingest.md: fix WebIngestor (rate_limit → delay, ingest() → ingest_url()); FeedIngestor (ingest() → ingest_feed(), monitor() → monitor_feeds()); StreamIngestor (backend= constructor → ingest_kafka/rabbitmq/kinesis/pulsar()); DBIngestor constructor + ingest() → ingest_database(); SnowflakeIngestor.ingest() → ingest_query()/ingest_table(); OntologyIngestor.ingest() → ingest_ontology(); DataSource → FileObject - explorer.md: remove start_explorer() Python function (only CLI exists); replace with semantica-explorer CLI usage - provenance.md: ActivityTracker → ProvenanceTracker in CardGroup - semantic_extract.md: EventExtractor → EventDetector - triplet_store.md: remove InMemoryTripletStore (doesn't exist); fix tip - llms.md: fix providers (Anthropic/Gemini/Ollama/DeepSeek/NovitaAI → LiteLLM); HuggingFace → HuggingFaceLLM; remove create_provider()
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2.0 KiB
title, description, icon
| title | description | icon |
|---|---|---|
| Evals Module | Evaluation framework for measuring Knowledge Graph quality, extraction accuracy, and pipeline performance — coming soon. | chart-line |
semantica.evals is planned as a comprehensive evaluation framework for measuring extraction accuracy, graph quality, and pipeline performance.
Planned Features
When released, semantica.evals will provide:
- KG quality metrics — completeness, consistency, schema compliance, coverage, and orphan node detection
- Extraction accuracy — NER precision / recall / F1 and relation extraction metrics against gold-standard datasets
- Pipeline benchmarking — throughput (docs/sec), per-step latency, peak memory, and error rate
- Regression tracking — record runs and compare metrics across commits or config changes
- Deduplication accuracy — merge precision, false positive / false negative rates
- Reasoning correctness — inference accuracy, rule coverage, and derivation depth
Current Workaround
Until semantica.evals ships, use semantica.ontology.OntologyEvaluator for ontology quality metrics:
from semantica.ontology import OntologyEvaluator
evaluator = OntologyEvaluator()
report = evaluator.evaluate(ontology, kg)
print(f"Coverage: {report.coverage:.2%}")
print(f"Completeness: {report.completeness:.2%}")