diff --git a/README.md b/README.md index ed5bf8de..1d2103b4 100644 --- a/README.md +++ b/README.md @@ -387,23 +387,19 @@ result = ExecutionEngine().execute_pipeline(pipeline, parallel=True) ### Production-Ready Quality Assurance -> **Enterprise-Grade QA** • Conflict Detection • Deduplication • Quality Scoring +> **Enterprise-Grade QA** • Conflict Detection • Deduplication ```python -from semantica.kg_qa import QualityAssessor from semantica.deduplication import DuplicateDetector from semantica.conflicts import ConflictDetector -assessor = QualityAssessor() -report = assessor.assess(kg, check_completeness=True, check_consistency=True) +conflicts = ConflictDetector().detect_conflicts(kg) +duplicates = DuplicateDetector().find_duplicates(entities=kg.entities, similarity_threshold=0.85) -detector = DuplicateDetector() -duplicates = detector.find_duplicates(entities=kg.entities, similarity_threshold=0.85) - -print(f"Quality Score: {report.overall_score}/100, Duplicates: {len(duplicates)}") +print(f"Conflicts: {len(conflicts)} | Duplicates: {len(duplicates)}") ``` -[**Cookbook: Conflict Detection**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/Conflict_Detection.ipynb) • [**Deduplication**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/Deduplication.ipynb) • [**Graph Quality**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/Graph_Quality.ipynb) +[**Cookbook: Conflict Detection**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/Conflict_Detection.ipynb) • [**Deduplication**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/Deduplication.ipynb) ## 🚀 Quick Start @@ -466,9 +462,10 @@ print(f"Answer: {result.answer} | Nodes: {kg.node_count}, Edges: {kg.edge_count} - [x] Core framework (v1.0) - [x] GraphRAG engine - [x] 6-stage ontology pipeline -- [x] Quality assurance features +- [] Quality assurance features - [ ] Enhanced multi-language support - [ ] Real-time streaming improvements +- [ ] `semantica.kg_qa` (KG Quality Assurance) module ### Q2 2026 - [ ] Multi-modal processing diff --git a/cookbook/advanced/03_Complete_Visualization_Suite.ipynb b/cookbook/advanced/03_Complete_Visualization_Suite.ipynb index 467e5511..5ce0269d 100644 --- a/cookbook/advanced/03_Complete_Visualization_Suite.ipynb +++ b/cookbook/advanced/03_Complete_Visualization_Suite.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -40,7 +47,7 @@ ")\n", "from semantica.kg import GraphBuilder, GraphAnalyzer\n", "from semantica.embeddings import EmbeddingGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "import numpy as np\n" ] }, diff --git a/cookbook/advanced/04_Conflict_Resolution_Strategies.ipynb b/cookbook/advanced/04_Conflict_Resolution_Strategies.ipynb index fb4dfa2a..ad9353ca 100644 --- a/cookbook/advanced/04_Conflict_Resolution_Strategies.ipynb +++ b/cookbook/advanced/04_Conflict_Resolution_Strategies.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -35,7 +42,7 @@ "outputs": [], "source": [ "from semantica.kg import GraphBuilder\n", - "from semantica.kg_qa import ConsistencyChecker\n", + "from datetime import datetime\n", "import json\n" ] diff --git a/cookbook/introduction/11_Graph_Quality.ipynb b/cookbook/introduction/11_Graph_Quality.ipynb index 411b7400..3a030e7a 100644 --- a/cookbook/introduction/11_Graph_Quality.ipynb +++ b/cookbook/introduction/11_Graph_Quality.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. The quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -45,11 +52,11 @@ "metadata": {}, "outputs": [], "source": [ - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.kg import GraphBuilder\n", "\n", "builder = GraphBuilder()\n", - "assessor = KGQualityAssessor()\n", + "\n", "entities = [\n", " {\"id\": \"e1\", \"type\": \"Organization\", \"name\": \"Apple Inc.\", \"properties\": {}}\n", @@ -81,7 +88,7 @@ "metadata": {}, "outputs": [], "source": [ - "from semantica.kg_qa import ConsistencyChecker\n", + "\n", "consistency_checker = ConsistencyChecker()\n", "\n", @@ -107,7 +114,7 @@ "metadata": {}, "outputs": [], "source": [ - "from semantica.kg_qa import CompletenessValidator\n", + "\n", "completeness_validator = CompletenessValidator()\n", "\n", @@ -133,7 +140,7 @@ "metadata": {}, "outputs": [], "source": [ - "from semantica.kg_qa import QualityMetrics\n", + "\n", "quality_metrics = QualityMetrics()\n", "\n", diff --git a/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb b/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb index 78e146e5..2eaf03dd 100644 --- a/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb +++ b/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb @@ -1402,7 +1402,7 @@ "outputs": [], "source": [ "from semantica.reasoning import InferenceEngine, RuleManager\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "\n", "# Advanced Feature 1: Reasoning with Inference Engine\n", "print(f\"Advanced Feature: Logical Reasoning\")\n", diff --git a/cookbook/use_cases/biomedical/02_Genomic_Variant_Analysis.ipynb b/cookbook/use_cases/biomedical/02_Genomic_Variant_Analysis.ipynb index 6e0e6e3d..aaa6a3bb 100644 --- a/cookbook/use_cases/biomedical/02_Genomic_Variant_Analysis.ipynb +++ b/cookbook/use_cases/biomedical/02_Genomic_Variant_Analysis.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -62,7 +69,7 @@ "from semantica.kg import ConnectivityAnalyzer, TemporalGraphQuery, TemporalPatternDetector\n", "from semantica.ontology import OntologyGenerator, ClassInferrer, PropertyGenerator, OntologyValidator\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.conflicts import ConflictDetector\n", "from semantica.export import JSONExporter, RDFExporter, OWLExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, OntologyVisualizer, AnalyticsVisualizer\n", diff --git a/cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb b/cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb index 5fd941d7..35c55857 100644 --- a/cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb +++ b/cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -61,7 +68,7 @@ "from semantica.kg import GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, GraphAnalyzer\n", "from semantica.kg import CentralityCalculator, CommunityDetector, ConnectivityAnalyzer\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.conflicts import ConflictDetector\n", "from semantica.export import JSONExporter, RDFExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n", diff --git a/cookbook/use_cases/cybersecurity/01_Anomaly_Detection_Real_Time.ipynb b/cookbook/use_cases/cybersecurity/01_Anomaly_Detection_Real_Time.ipynb index 7439b7b9..29d7a572 100644 --- a/cookbook/use_cases/cybersecurity/01_Anomaly_Detection_Real_Time.ipynb +++ b/cookbook/use_cases/cybersecurity/01_Anomaly_Detection_Real_Time.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -60,7 +67,7 @@ "from semantica.kg import GraphBuilder, TemporalPatternDetector, TemporalGraphQuery, GraphAnalyzer\n", "from semantica.kg import CentralityCalculator, CommunityDetector, ConnectivityAnalyzer\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor, AutomatedFixer\n", + "from semantica.export import JSONExporter, CSVExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n", "import tempfile\n", @@ -483,4 +490,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/cookbook/use_cases/cybersecurity/02_Incident_Analysis.ipynb b/cookbook/use_cases/cybersecurity/02_Incident_Analysis.ipynb index 9db3e088..9fa5e1a7 100644 --- a/cookbook/use_cases/cybersecurity/02_Incident_Analysis.ipynb +++ b/cookbook/use_cases/cybersecurity/02_Incident_Analysis.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -58,7 +65,7 @@ "from semantica.semantic_extract import NERExtractor, RelationExtractor, EventDetector, TripleExtractor\n", "from semantica.kg import GraphBuilder, GraphAnalyzer, ConnectivityAnalyzer, CentralityCalculator\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.conflicts import ConflictDetector\n", "from semantica.kg import ProvenanceTracker\n", "from semantica.export import JSONExporter, RDFExporter, ReportGenerator\n", @@ -463,4 +470,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/cookbook/use_cases/cybersecurity/03_Threat_Correlation.ipynb b/cookbook/use_cases/cybersecurity/03_Threat_Correlation.ipynb index a80beeb9..50684d52 100644 --- a/cookbook/use_cases/cybersecurity/03_Threat_Correlation.ipynb +++ b/cookbook/use_cases/cybersecurity/03_Threat_Correlation.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -58,7 +65,7 @@ "from semantica.semantic_extract import NERExtractor, RelationExtractor, EventDetector\n", "from semantica.kg import GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, GraphAnalyzer, ConnectivityAnalyzer\n", "from semantica.reasoning import InferenceEngine, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.kg import ProvenanceTracker\n", "from semantica.conflicts import ConflictDetector\n", "from semantica.export import RDFExporter, ReportGenerator\n", diff --git a/cookbook/use_cases/cybersecurity/06_Vulnerability_Tracking.ipynb b/cookbook/use_cases/cybersecurity/06_Vulnerability_Tracking.ipynb index ca0418c2..800d1681 100644 --- a/cookbook/use_cases/cybersecurity/06_Vulnerability_Tracking.ipynb +++ b/cookbook/use_cases/cybersecurity/06_Vulnerability_Tracking.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -60,7 +67,7 @@ "from semantica.kg import GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, GraphAnalyzer\n", "from semantica.kg import CentralityCalculator, CommunityDetector, ConnectivityAnalyzer\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.conflicts import ConflictDetector\n", "from semantica.export import JSONExporter, RDFExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n", diff --git a/cookbook/use_cases/finance/02_Financial_Reports_Analysis.ipynb b/cookbook/use_cases/finance/02_Financial_Reports_Analysis.ipynb index 8bda57cd..62e60391 100644 --- a/cookbook/use_cases/finance/02_Financial_Reports_Analysis.ipynb +++ b/cookbook/use_cases/finance/02_Financial_Reports_Analysis.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -60,7 +67,7 @@ "from semantica.kg import GraphBuilder, GraphAnalyzer, CentralityCalculator, CommunityDetector\n", "from semantica.kg import ConnectivityAnalyzer, TemporalGraphQuery, TemporalPatternDetector\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.conflicts import ConflictDetector\n", "from semantica.export import JSONExporter, CSVExporter, RDFExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, AnalyticsVisualizer, TemporalVisualizer\n", @@ -425,4 +432,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/cookbook/use_cases/finance/03_Fraud_Detection.ipynb b/cookbook/use_cases/finance/03_Fraud_Detection.ipynb index 871a7801..e998c893 100644 --- a/cookbook/use_cases/finance/03_Fraud_Detection.ipynb +++ b/cookbook/use_cases/finance/03_Fraud_Detection.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -59,7 +66,7 @@ "from semantica.semantic_extract import NERExtractor, RelationExtractor, EventDetector\n", "from semantica.kg import GraphBuilder, TemporalPatternDetector, GraphAnalyzer\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor, AutomatedFixer\n", + "from semantica.export import JSONExporter, CSVExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n", "import tempfile\n", diff --git a/cookbook/use_cases/finance/05_Regulatory_Compliance.ipynb b/cookbook/use_cases/finance/05_Regulatory_Compliance.ipynb index 1440254f..adc2b90b 100644 --- a/cookbook/use_cases/finance/05_Regulatory_Compliance.ipynb +++ b/cookbook/use_cases/finance/05_Regulatory_Compliance.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -60,7 +67,7 @@ "from semantica.kg import GraphBuilder, GraphAnalyzer, ConnectivityAnalyzer\n", "from semantica.ontology import OntologyGenerator, ClassInferrer, PropertyGenerator, OntologyValidator\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor, ValidationEngine\n", + "from semantica.conflicts import ConflictDetector\n", "from semantica.export import JSONExporter, CSVExporter, RDFExporter, OWLExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, OntologyVisualizer, AnalyticsVisualizer\n", diff --git a/cookbook/use_cases/healthcare/01_Clinical_Reports_Processing.ipynb b/cookbook/use_cases/healthcare/01_Clinical_Reports_Processing.ipynb index 5b5ac29d..83d84ebf 100644 --- a/cookbook/use_cases/healthcare/01_Clinical_Reports_Processing.ipynb +++ b/cookbook/use_cases/healthcare/01_Clinical_Reports_Processing.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -60,7 +67,7 @@ "from semantica.kg import GraphBuilder, GraphValidator, EntityResolver, GraphAnalyzer\n", "from semantica.triple_store import TripleStore, TripleManager, QueryEngine\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor, ValidationEngine\n", + "from semantica.export import JSONExporter, RDFExporter, OWLExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, OntologyVisualizer, TemporalVisualizer\n", "import tempfile\n", diff --git a/cookbook/use_cases/healthcare/02_Disease_Network_Analysis.ipynb b/cookbook/use_cases/healthcare/02_Disease_Network_Analysis.ipynb index 5bb26e91..d415c6ad 100644 --- a/cookbook/use_cases/healthcare/02_Disease_Network_Analysis.ipynb +++ b/cookbook/use_cases/healthcare/02_Disease_Network_Analysis.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -62,7 +69,7 @@ "from semantica.kg import ConnectivityAnalyzer, TemporalGraphQuery, TemporalPatternDetector\n", "from semantica.ontology import OntologyGenerator, ClassInferrer, PropertyGenerator, OntologyValidator\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.conflicts import ConflictDetector\n", "from semantica.export import JSONExporter, RDFExporter, OWLExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, OntologyVisualizer, AnalyticsVisualizer\n", @@ -414,4 +421,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/cookbook/use_cases/healthcare/03_Drug_Interactions_Analysis.ipynb b/cookbook/use_cases/healthcare/03_Drug_Interactions_Analysis.ipynb index ccb278f4..30fb05ce 100644 --- a/cookbook/use_cases/healthcare/03_Drug_Interactions_Analysis.ipynb +++ b/cookbook/use_cases/healthcare/03_Drug_Interactions_Analysis.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -62,7 +69,7 @@ "from semantica.kg import ConnectivityAnalyzer, TemporalGraphQuery\n", "from semantica.ontology import OntologyGenerator, ClassInferrer, PropertyGenerator, OntologyValidator\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.conflicts import ConflictDetector\n", "from semantica.export import JSONExporter, RDFExporter, OWLExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, OntologyVisualizer, AnalyticsVisualizer\n", @@ -424,4 +431,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/cookbook/use_cases/healthcare/05_Medical_Database_Integration.ipynb b/cookbook/use_cases/healthcare/05_Medical_Database_Integration.ipynb index 3947b596..4c0f4628 100644 --- a/cookbook/use_cases/healthcare/05_Medical_Database_Integration.ipynb +++ b/cookbook/use_cases/healthcare/05_Medical_Database_Integration.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -62,7 +69,7 @@ "from semantica.kg import GraphBuilder, GraphValidator, EntityResolver, GraphAnalyzer\n", "from semantica.triple_store import TripleStore, TripleManager, QueryEngine\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor, ValidationEngine\n", + "from semantica.export import JSONExporter, RDFExporter, OWLExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, OntologyVisualizer, TemporalVisualizer\n", "import json\n", diff --git a/cookbook/use_cases/renewable_energy/01_Energy_Market_Analysis.ipynb b/cookbook/use_cases/renewable_energy/01_Energy_Market_Analysis.ipynb index 11bbbcb7..7afbdd70 100644 --- a/cookbook/use_cases/renewable_energy/01_Energy_Market_Analysis.ipynb +++ b/cookbook/use_cases/renewable_energy/01_Energy_Market_Analysis.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -60,7 +67,7 @@ "from semantica.kg import GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, GraphAnalyzer\n", "from semantica.kg import CentralityCalculator, CommunityDetector, ConnectivityAnalyzer\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.export import JSONExporter, CSVExporter, RDFExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n", "import tempfile\n", @@ -439,4 +446,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/cookbook/use_cases/renewable_energy/02_Environmental_Impact.ipynb b/cookbook/use_cases/renewable_energy/02_Environmental_Impact.ipynb index 65d80283..1875d954 100644 --- a/cookbook/use_cases/renewable_energy/02_Environmental_Impact.ipynb +++ b/cookbook/use_cases/renewable_energy/02_Environmental_Impact.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -62,7 +69,7 @@ "from semantica.kg import ConnectivityAnalyzer, TemporalGraphQuery, TemporalPatternDetector\n", "from semantica.ontology import OntologyGenerator, ClassInferrer, PropertyGenerator, OntologyValidator\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.conflicts import ConflictDetector\n", "from semantica.export import JSONExporter, CSVExporter, RDFExporter, OWLExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, OntologyVisualizer, AnalyticsVisualizer\n", diff --git a/cookbook/use_cases/renewable_energy/03_Grid_Management.ipynb b/cookbook/use_cases/renewable_energy/03_Grid_Management.ipynb index cc08e879..c437d185 100644 --- a/cookbook/use_cases/renewable_energy/03_Grid_Management.ipynb +++ b/cookbook/use_cases/renewable_energy/03_Grid_Management.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -60,7 +67,7 @@ "from semantica.kg import GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, GraphAnalyzer\n", "from semantica.kg import CentralityCalculator, CommunityDetector, ConnectivityAnalyzer\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor, AutomatedFixer\n", + "from semantica.export import JSONExporter, CSVExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n", "import tempfile\n", @@ -467,4 +474,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/cookbook/use_cases/renewable_energy/04_Resource_Optimization.ipynb b/cookbook/use_cases/renewable_energy/04_Resource_Optimization.ipynb index 804e4b35..8d18541b 100644 --- a/cookbook/use_cases/renewable_energy/04_Resource_Optimization.ipynb +++ b/cookbook/use_cases/renewable_energy/04_Resource_Optimization.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -60,7 +67,7 @@ "from semantica.kg import GraphBuilder, GraphAnalyzer, CentralityCalculator, CommunityDetector\n", "from semantica.kg import ConnectivityAnalyzer, TemporalGraphQuery, TemporalPatternDetector\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.conflicts import ConflictDetector\n", "from semantica.export import JSONExporter, CSVExporter, RDFExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, AnalyticsVisualizer, TemporalVisualizer\n", diff --git a/cookbook/use_cases/trading/01_Market_Data_Analysis.ipynb b/cookbook/use_cases/trading/01_Market_Data_Analysis.ipynb index 34ef15a7..477ee767 100644 --- a/cookbook/use_cases/trading/01_Market_Data_Analysis.ipynb +++ b/cookbook/use_cases/trading/01_Market_Data_Analysis.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -60,7 +67,7 @@ "from semantica.kg import GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, GraphAnalyzer\n", "from semantica.kg import CentralityCalculator, CommunityDetector, ConnectivityAnalyzer\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.export import JSONExporter, CSVExporter, RDFExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n", "import tempfile\n", @@ -364,4 +371,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/cookbook/use_cases/trading/02_News_Sentiment_Analysis.ipynb b/cookbook/use_cases/trading/02_News_Sentiment_Analysis.ipynb index 0ef465b3..c78be5d9 100644 --- a/cookbook/use_cases/trading/02_News_Sentiment_Analysis.ipynb +++ b/cookbook/use_cases/trading/02_News_Sentiment_Analysis.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -62,7 +69,7 @@ "from semantica.kg import ConnectivityAnalyzer, TemporalGraphQuery, TemporalPatternDetector\n", "from semantica.embeddings import EmbeddingGenerator, TextEmbedder\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.export import JSONExporter, CSVExporter, RDFExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n", "import tempfile\n", diff --git a/cookbook/use_cases/trading/03_Real_Time_Monitoring.ipynb b/cookbook/use_cases/trading/03_Real_Time_Monitoring.ipynb index 48e0fd3b..67fbe361 100644 --- a/cookbook/use_cases/trading/03_Real_Time_Monitoring.ipynb +++ b/cookbook/use_cases/trading/03_Real_Time_Monitoring.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -60,7 +67,7 @@ "from semantica.kg import GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, GraphAnalyzer\n", "from semantica.kg import CentralityCalculator, CommunityDetector, ConnectivityAnalyzer\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor, AutomatedFixer\n", + "from semantica.export import JSONExporter, CSVExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n", "import tempfile\n", @@ -406,4 +413,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/cookbook/use_cases/trading/04_Risk_Assessment.ipynb b/cookbook/use_cases/trading/04_Risk_Assessment.ipynb index 17a49070..2e63875a 100644 --- a/cookbook/use_cases/trading/04_Risk_Assessment.ipynb +++ b/cookbook/use_cases/trading/04_Risk_Assessment.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/cookbook/use_cases/trading/05_Strategy_Backtesting.ipynb b/cookbook/use_cases/trading/05_Strategy_Backtesting.ipynb index 356d41ef..3f62b34b 100644 --- a/cookbook/use_cases/trading/05_Strategy_Backtesting.ipynb +++ b/cookbook/use_cases/trading/05_Strategy_Backtesting.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -60,7 +67,7 @@ "from semantica.kg import GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, GraphAnalyzer\n", "from semantica.kg import CentralityCalculator, CommunityDetector, ConnectivityAnalyzer\n", "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n", - "from semantica.kg_qa import KGQualityAssessor\n", + "from semantica.export import JSONExporter, CSVExporter, RDFExporter, ReportGenerator\n", "from semantica.visualization import KGVisualizer, TemporalVisualizer, AnalyticsVisualizer\n", "import tempfile\n", @@ -418,4 +425,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/docs/LIBS_README.md b/docs/LIBS_README.md index bc147615..32e2a478 100644 --- a/docs/LIBS_README.md +++ b/docs/LIBS_README.md @@ -820,42 +820,7 @@ result = execution_engine.execute_pipeline(custom_pipeline) ### 6. Quality Assurance Examples -#### Knowledge Graph Quality Assessment -```python -from semantica.kg_qa import KGQualityAssessor, ValidationEngine - -# Initialize quality assessor -quality_assessor = KGQualityAssessor( - config={ - "consistency": {"enable": True}, - "completeness": {"enable": True}, - "validation": {"strict": True} - } -) - -# Assess knowledge graph quality -quality_report = quality_assessor.assess_quality(graph) - -print(f"Overall Quality Score: {quality_report.overall_score:.2f}") -print(f"Consistency Score: {quality_report.consistency_score:.2f}") -print(f"Completeness Score: {quality_report.completeness_score:.2f}") - -# Get issues -for issue in quality_report.issues: - print(f"Issue: {issue.type}") - print(f"Severity: {issue.severity}") - print(f"Description: {issue.description}") - print() - -# Validate graph -validation_engine = ValidationEngine() -validation_result = validation_engine.validate(graph) - -if validation_result.valid: - print("Graph is valid!") -else: - print(f"Validation errors: {validation_result.errors}") -``` +Note: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. ### 7. Export Examples @@ -902,7 +867,6 @@ from semantica.ingest import FileIngestor from semantica.semantic_extract import NERExtractor, RelationExtractor from semantica.embeddings import EmbeddingGenerator from semantica.kg import GraphBuilder -from semantica.kg_qa import KGQualityAssessor from semantica.export import JSONExporter # No explicit initialization needed - framework auto-initializes on first use @@ -948,10 +912,8 @@ graph = graph_builder.build({ "relationships": all_relationships }) -# Step 5: Assess quality -quality_assessor = KGQualityAssessor() -quality_report = quality_assessor.assess_quality(graph) -print(f"Knowledge Graph Quality: {quality_report.overall_score:.2f}") +# Step 5: (Optional) Quality assessment is temporarily unavailable +# The `semantica.kg_qa` module will be reintroduced in a future release. # Step 6: Export results json_exporter = JSONExporter() @@ -1142,10 +1104,7 @@ from semantica.visualization import QualityVisualizer quality_viz = QualityVisualizer() # Quality dashboard -from semantica.kg_qa import KGQualityAssessor -quality_assessor = KGQualityAssessor() -quality_report = quality_assessor.generate_quality_report(graph) - +quality_report = {"overall_score": 0.85, "issues": [], "consistency": {}, "completeness": {}} quality_viz.visualize_dashboard(quality_report, output="html", file_path="quality_dashboard.html") # Quality score distribution @@ -1156,17 +1115,13 @@ quality_viz.visualize_score_distribution(quality_scores, # Quality issues quality_viz.visualize_issues(quality_report, output="html", file_path="quality_issues.html") -# Completeness metrics -from semantica.kg_qa import CompletenessMetrics -completeness_metrics = CompletenessMetrics() -completeness_data = completeness_metrics.calculate_entity_completeness(entities, schema) +# Completeness metrics (provide your precomputed data) +completeness_data = {"score": 0.82, "by_type": {"Person": 0.9, "Company": 0.75}} quality_viz.visualize_completeness_metrics(completeness_data, output="html", file_path="completeness.html") -# Consistency heatmap -from semantica.kg_qa import ConsistencyMetrics -consistency_metrics = ConsistencyMetrics() -consistency_data = consistency_metrics.calculate_logical_consistency(graph) +# Consistency heatmap (provide your precomputed data) +consistency_data = {"score": 0.88, "violations": []} quality_viz.visualize_consistency_heatmap(consistency_data, output="html", file_path="consistency_heatmap.html") ``` diff --git a/docs/architecture.md b/docs/architecture.md index dbe483a2..67184ca6 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -73,7 +73,6 @@ graph TB ### Quality Assurance - **`semantica.deduplication`** - Entity deduplication - **`semantica.conflicts`** - Conflict detection and resolution -- **`semantica.kg_qa`** - Quality assessment --- @@ -118,14 +117,7 @@ class CustomExtractor(BaseExtractor): ### Custom Validators -```python -from semantica.kg_qa import BaseValidator - -class CustomValidator(BaseValidator): - def validate(self, graph): - # Custom validation logic - pass -``` +Validators can be implemented within domain-specific modules (e.g., graph or ontology) as needed. --- diff --git a/docs/modules.md b/docs/modules.md index 7411cd71..41a7dc08 100644 --- a/docs/modules.md +++ b/docs/modules.md @@ -16,7 +16,7 @@ Semantica's modules are organized into six logical layers: | **Input Layer** | [Ingest](#ingest-module), [Parse](#parse-module), [Split](#split-module), [Normalize](#normalize-module) | Data ingestion, parsing, chunking, and cleaning | | **Core Processing** | [Semantic Extract](#semantic-extract-module), [Knowledge Graph](#knowledge-graph-kg-module), [Ontology](#ontology-module), [Reasoning](#reasoning-module) | Entity extraction, graph construction, inference | | **Storage** | [Embeddings](#embeddings-module), [Vector Store](#vector-store-module), [Graph Store](#graph-store-module), [Triple Store](#triple-store-module) | Vector and graph persistence | -| **Quality Assurance** | [Deduplication](#deduplication-module), [Conflicts](#conflicts-module), [KG QA](#kg-quality-assurance-module) | Data quality and consistency | +| **Quality Assurance** | [Deduplication](#deduplication-module), [Conflicts](#conflicts-module) | Data quality and consistency | | **Context & Memory** | [Context](#context-module), [Seed](#seed-module) | Agent memory and foundation data | | **Output & Orchestration** | [Export](#export-module), [Visualization](#visualization-module), [Pipeline](#pipeline-module) | Export, visualization, and workflow management | @@ -765,27 +765,7 @@ conflicts = detector.detect_value_conflicts(entities, "name") - `AutoMerger` — Automatic merging of duplicates - `AutoResolver` — Automatic conflict resolution -**Quality Metrics:** - -| Metric | Calculation | -| :--- | :--- | -| **Overall Score** | `(0.6 × completeness) + (0.4 × consistency)` | -| **Entity Quality** | Required field presence (ID, type) | -| **Relationship Quality** | Required field presence (source, target, type) | - -**Quick Example:** - -```python -from semantica.kg_qa import assess_quality, generate_quality_report, KGQualityAssessor - -# Using convenience functions -score = assess_quality(knowledge_graph) -report = generate_quality_report(knowledge_graph, schema) - -# Using classes directly -assessor = KGQualityAssessor() -score = assessor.assess_overall_quality(knowledge_graph) -``` +Note: The KG quality assessment module has been temporarily removed and will be reintroduced in a future release. --- @@ -1138,7 +1118,6 @@ new_facts = inference_engine.forward_chain(kg, rule_manager) | **Triple Store** | `semantica.triple_store` | `TripleManager` | RDF storage | | **Deduplication** | `semantica.deduplication` | `DuplicateDetector` | Duplicate removal | | **Conflicts** | `semantica.conflicts` | `ConflictDetector` | Conflict resolution | -| **KG QA** | `semantica.kg_qa` | `KGQualityAssessor` | Quality assurance | | **Context** | `semantica.context` | `AgentMemory` | Agent context | | **Seed** | `semantica.seed` | `SeedDataManager` | Foundation data | | **Export** | `semantica.export` | `JSONExporter` | Data export | diff --git a/docs/reference/kg_qa.md b/docs/reference/kg_qa.md deleted file mode 100644 index a2a507ba..00000000 --- a/docs/reference/kg_qa.md +++ /dev/null @@ -1,216 +0,0 @@ -# KG QA - -> **Knowledge Graph Quality Assurance system for validation, metrics, and automated repair.** - ---- - -## 🎯 Overview - -
- -- :material-check-decagram:{ .lg .middle } **Quality Metrics** - - --- - - Calculate Completeness, Consistency, and Accuracy scores - -- :material-shield-check:{ .lg .middle } **Validation Engine** - - --- - - Validate against schema constraints and custom rules - -- :material-wrench:{ .lg .middle } **Automated Fixes** - - --- - - Auto-repair duplicates, missing fields, and inconsistencies - -- :material-file-document-edit:{ .lg .middle } **Reporting** - - --- - - Generate detailed quality reports (JSON, HTML, YAML) - -- :material-relation-many-to-many:{ .lg .middle } **Consistency** - - --- - - Check logical, temporal, and hierarchical consistency - -- :material-lightbulb:{ .lg .middle } **Suggestions** - - --- - - Get actionable improvement suggestions - -
- -!!! tip "When to Use" - - **Pre-Deployment**: Validate graph quality before production use - - **Monitoring**: Continuous quality monitoring of live graphs - - **Debugging**: Identify and fix issues in problematic graphs - ---- - -## ⚙️ Algorithms Used - -### Quality Metrics -- **Weighted Averaging**: `Score = w1*Completeness + w2*Consistency` -- **Normalization**: Min-max scaling of scores to `0.0 - 1.0` -- **Completeness Ratio**: `PresentProperties / RequiredProperties` - -### Consistency Checking -- **Logical Consistency**: Contradiction detection (e.g., A > B and B > A) -- **Temporal Consistency**: Time range validation (Start < End) -- **Hierarchical Consistency**: Cycle detection in taxonomy (DFS) -- **Domain/Range**: Type compatibility checking for relationships - -### Automated Fixes -- **Duplicate Merging**: Using Deduplication module strategies -- **Conflict Resolution**: Using Conflicts module strategies -- **Default Injection**: Filling missing required fields with defaults -- **Inference**: Inferring missing types or links based on topology - ---- - -## Main Classes - -### KGQualityAssessor - -Coordinator for overall quality assessment. - -**Methods:** - -| Method | Description | -|--------|-------------| -| `assess_quality(kg)` | Calculate all metrics | -| `generate_report(kg)` | Create full report | - -**Example:** - -```python -from semantica.kg_qa import KGQualityAssessor - -assessor = KGQualityAssessor() -score = assessor.assess_overall_quality(kg) -print(f"Graph Quality Score: {score}") -``` - -### ConsistencyChecker - -Validates graph consistency. - -**Methods:** - -| Method | Description | Algorithm | -|--------|-------------|-----------| -| `check_logical(kg)` | Logical rules | Rule Engine | -| `check_temporal(kg)` | Time validity | Range Check | -| `check_hierarchical(kg)` | Cycles/Tree | DFS | - -### CompletenessValidator - -Checks for missing data. - -**Methods:** - -| Method | Description | -|--------|-------------| -| `validate_entities(kg)` | Check entity fields | -| `validate_schema(kg)` | Check schema compliance | - -### AutomatedFixer - -Applies automatic repairs. - -**Methods:** - -| Method | Description | -|--------|-------------| -| `fix_issues(kg, issues)` | Fix reported issues | -| `merge_duplicates(kg)` | Fix duplicates | -| `resolve_conflicts(kg)` | Fix conflicts | - ---- - -## Convenience Functions - -```python -from semantica.kg_qa import assess_quality, generate_quality_report, fix_issues - -# 1. Assess -score = assess_quality(kg) - -# 2. Report -report = generate_quality_report(kg, schema=my_schema) - -# 3. Fix -fixed_kg = fix_issues(kg, report.issues) -``` - ---- - -## Configuration - -### Environment Variables - -```bash -export KG_QA_MIN_SCORE=0.7 -export KG_QA_STRICT_MODE=true -``` - -### YAML Configuration - -```yaml -kg_qa: - thresholds: - overall: 0.7 - completeness: 0.8 - consistency: 0.9 - - weights: - completeness: 0.6 - consistency: 0.4 - - auto_fix: - enabled: true - strategies: - duplicates: merge - missing_fields: default -``` - ---- - -## Integration Examples - -### CI/CD Pipeline - -```python -from semantica.kg_qa import assess_quality - -def validate_graph_deployment(kg): - score = assess_quality(kg) - - if score < 0.8: - raise ValueError(f"Quality score {score} too low for deployment!") - - print("Graph passed quality checks.") -``` - ---- - -## Best Practices - -1. **Define Schema**: QA is most effective when validated against a strict schema (Ontology). -2. **Run Regularly**: Graph quality degrades over time; run QA jobs periodically. -3. **Review Fixes**: Automated fixes are powerful but verify them for critical data. -4. **Handle Warnings**: Don't ignore warnings; they often indicate creeping data quality issues. - ---- - -## See Also - -- [Ontology Module](ontology.md) - Defining schemas for validation -- [Deduplication Module](deduplication.md) - Used for fixing duplicates -- [Conflicts Module](conflicts.md) - Used for resolving inconsistencies diff --git a/semantica/__init__.py b/semantica/__init__.py index b33112f0..0232bb9f 100644 --- a/semantica/__init__.py +++ b/semantica/__init__.py @@ -22,24 +22,6 @@ from typing import Any, Dict, List, Optional, Union # Core imports from .core import Config, ConfigManager, LifecycleManager, PluginRegistry, Semantica -# KG Quality Assurance -from .kg_qa import ( - AutomatedFixer, - AutoMerger, - AutoResolver, - CompletenessMetrics, - CompletenessValidator, - ConsistencyChecker, - ConsistencyMetrics, - ConstraintValidator, - ImprovementSuggestions, - IssueTracker, - KGQualityAssessor, - QualityMetrics, - QualityReporter, - RuleValidator, - ValidationEngine, -) # Pipeline imports from .pipeline import ( @@ -142,13 +124,6 @@ class _SemanticaModules: self._visualization = _ModuleProxy("visualization") return self._visualization - @property - def kg_qa(self): - """Access KG quality assurance module.""" - if self._kg_qa is None: - self._kg_qa = _ModuleProxy("kg_qa") - return self._kg_qa - @property def pipeline(self): """Access pipeline module.""" @@ -289,22 +264,6 @@ __all__ = [ "ParallelismManager", "ResourceScheduler", "PipelineValidator", - # KG Quality Assurance - "KGQualityAssessor", - "ConsistencyChecker", - "CompletenessValidator", - "QualityMetrics", - "CompletenessMetrics", - "ConsistencyMetrics", - "ValidationEngine", - "RuleValidator", - "ConstraintValidator", - "QualityReporter", - "IssueTracker", - "ImprovementSuggestions", - "AutomatedFixer", - "AutoMerger", - "AutoResolver", # Visualization "KGVisualizer", "OntologyVisualizer", @@ -325,7 +284,6 @@ def __getattr__(name: str): "embeddings", "semantic_extract", "visualization", - "kg_qa", "pipeline", "parse", "normalize", diff --git a/semantica/kg_qa/__init__.py b/semantica/kg_qa/__init__.py deleted file mode 100644 index b9dcf7c9..00000000 --- a/semantica/kg_qa/__init__.py +++ /dev/null @@ -1,180 +0,0 @@ -""" -Knowledge Graph Quality Assurance Module - -This module provides comprehensive quality assurance capabilities for the -Semantica framework, enabling production-ready knowledge graph quality -assessment, validation, and automated fixes. - -Algorithms Used: - -Quality Metrics Calculation: - - Weighted Averaging: Overall quality score aggregation using weighted average formula: overall = (0.6 * completeness) + (0.4 * consistency) - - Entity Quality Scoring: Required field presence checking (ID/URI, type), binary scoring (0.5 per field), average calculation across entities: sum(scores) / len(scores) - - Relationship Quality Scoring: Required field presence checking (source/subject, target/object, type/predicate), weighted scoring (0.33 per field), average calculation across relationships - - Score Normalization: Min-max normalization with clamping to 0.0-1.0 range: min(1.0, max(0.0, score)) - - Consistency Score Calculation: Logical inconsistency detection (placeholder for reasoner-based checking) - -Completeness Metrics: - - Entity Completeness Calculation: Schema-based required property validation, ratio calculation present_props / required_props, average completeness across entities - - Relationship Completeness Calculation: Required field validation (source, target, type), completeness ratio (has_source + has_target + has_type) / 3.0, average across relationships - - Property Completeness Calculation: Schema-based property validation per entity type, completeness ratio calculation, average across entity types - - Schema Constraint Matching: Entity type to constraint mapping, required property extraction from schema constraints - -Consistency Metrics: - - Logical Consistency Checking: Contradiction detection, conflicting relationship identification, inconsistent property value detection (placeholder for reasoner integration) - - Temporal Consistency Checking: Temporal contradiction detection, invalid time range validation, conflicting temporal relationship identification - - Hierarchical Consistency Checking: Circular inheritance detection (DFS-based cycle detection), invalid parent-child relationship validation, hierarchical structure validation - -Validation Engine: - - Rule-Based Validation: Custom rule function execution, rule result parsing (error/warning extraction from dict), exception handling and error collection - - Constraint-Based Validation: Entity constraint validation (required properties), relationship constraint validation (domain and range), constraint matching algorithms - - Domain and Range Validation: Relationship type to domain/range mapping, entity type compatibility checking - - Validation Result Aggregation: Error and warning collection, validity determination (valid = len(errors) == 0) - -Quality Reporting: - - Issue Identification: Threshold-based issue detection (overall < 0.7, completeness < 0.8), issue type classification (quality, completeness, consistency), severity assignment (low, medium, high) - - Recommendation Generation: Issue-based recommendation generation, score-based recommendation generation, actionable suggestion creation - - Report Serialization: JSON serialization (ISO timestamp formatting, nested structure), YAML serialization (with PyYAML fallback), HTML report generation (planned) - - Issue Tracking: Dictionary-based issue storage (ID as key), severity-based filtering, issue resolution tracking - -Automated Fixes: - - Duplicate Detection: Entity duplicate identification (using deduplication module), relationship duplicate identification (same source, target, type matching) - - Duplicate Merging: Property aggregation strategies, relationship reference updating, entity consolidation - - Conflict Resolution: Conflicting property value detection, resolution strategy selection (highest confidence, most recent, source-based), conflict merging - - Missing Property Completion: Schema-based required property identification, default value assignment, value inference from context (planned) - - Inconsistency Resolution: Logical inconsistency detection, resolution strategy application, graph update - -Quality Assessment Coordination: - - Metric Aggregation: Multi-metric collection (overall, completeness, consistency), score combination, report generation coordination - - Component Integration: Quality metrics integration, validation engine integration, reporting integration, automated fixing integration - -Key Features: - - Quality metrics calculation (overall, completeness, consistency) - - Consistency checking (logical, temporal, hierarchical) - - Completeness validation (entity, relationship, property) - - Automated fixes (duplicates, inconsistencies, missing properties) - - Quality reporting with issue tracking - - Validation engine with rules and constraints - - Method registry for extensibility - - Configuration management with environment variables and config files - -Main Classes: - - KGQualityAssessor: Overall quality assessment coordinator - - ConsistencyChecker: Consistency validation engine - - CompletenessValidator: Completeness validation engine - - QualityMetrics: Quality metrics calculator - - CompletenessMetrics: Completeness metrics calculator - - ConsistencyMetrics: Consistency metrics calculator - - ValidationEngine: Rule and constraint validation - - RuleValidator: Rule-based validation - - ConstraintValidator: Constraint-based validation - - QualityReporter: Quality report generation - - IssueTracker: Issue tracking and management - - ImprovementSuggestions: Improvement suggestions generator - - AutomatedFixer: Automated issue fixing - - AutoMerger: Automatic merging of duplicates and conflicts - - AutoResolver: Automatic conflict and inconsistency resolution - - MethodRegistry: Registry for custom QA methods - - KGQAConfig: Configuration manager for KG QA module - -Convenience Functions: - - assess_quality: Quality assessment wrapper - - generate_quality_report: Quality report generation wrapper - - identify_quality_issues: Quality issue identification wrapper - - check_consistency: Consistency checking wrapper - - validate_completeness: Completeness validation wrapper - - calculate_quality_metrics: Quality metrics calculation wrapper - - validate_graph: Graph validation wrapper - - export_report: Report export wrapper - - fix_issues: Automated fixing wrapper - - get_qa_method: Get QA method by name - - list_available_methods: List registered methods - -Example Usage: - >>> from semantica.kg_qa import assess_quality, generate_quality_report, KGQualityAssessor - >>> # Using convenience functions - >>> score = assess_quality(knowledge_graph, method="default") - >>> report = generate_quality_report(knowledge_graph, schema, method="default") - >>> # Using classes directly - >>> from semantica.kg_qa import KGQualityAssessor - >>> assessor = KGQualityAssessor() - >>> score = assessor.assess_overall_quality(knowledge_graph) - >>> report = assessor.generate_quality_report(knowledge_graph, schema) - -Author: Semantica Contributors -License: MIT -""" - -from typing import Any, Dict, List, Optional, Union - -from .automated_fixes import AutomatedFixer, AutoMerger, AutoResolver, FixResult -from .config import KGQAConfig, kg_qa_config -from .kg_quality_assessor import ( - CompletenessValidator, - ConsistencyChecker, - KGQualityAssessor, -) -from .methods import ( - assess_quality, - calculate_quality_metrics, - check_consistency, - export_report, - fix_issues, - generate_quality_report, - get_qa_method, - identify_quality_issues, - list_available_methods, - validate_completeness, - validate_graph, -) -from .quality_metrics import CompletenessMetrics, ConsistencyMetrics, QualityMetrics -from .registry import MethodRegistry, method_registry -from .reporting import ( - ImprovementSuggestions, - IssueTracker, - QualityReport, - QualityReporter, -) -from .validation_engine import ConstraintValidator, RuleValidator, ValidationEngine - -__all__ = [ - # Main classes - "KGQualityAssessor", - "ConsistencyChecker", - "CompletenessValidator", - # Quality metrics - "QualityMetrics", - "CompletenessMetrics", - "ConsistencyMetrics", - # Validation - "ValidationEngine", - "RuleValidator", - "ConstraintValidator", - # Reporting - "QualityReporter", - "IssueTracker", - "ImprovementSuggestions", - "QualityReport", - # Automated fixes - "AutomatedFixer", - "AutoMerger", - "AutoResolver", - "FixResult", - # Registry and Methods - "MethodRegistry", - "method_registry", - "assess_quality", - "generate_quality_report", - "identify_quality_issues", - "check_consistency", - "validate_completeness", - "calculate_quality_metrics", - "validate_graph", - "export_report", - "fix_issues", - "get_qa_method", - "list_available_methods", - # Configuration - "KGQAConfig", - "kg_qa_config", -] diff --git a/semantica/kg_qa/automated_fixes.py b/semantica/kg_qa/automated_fixes.py deleted file mode 100644 index 1683f270..00000000 --- a/semantica/kg_qa/automated_fixes.py +++ /dev/null @@ -1,377 +0,0 @@ -""" -Automated Fixes Module - -This module provides automated fixing capabilities for the Semantica framework, -enabling automatic resolution of common knowledge graph quality issues. - -Key Features: - - Duplicate entity and relationship fixing - - Inconsistency resolution - - Missing property completion - - Conflicting property merging - - Conflict and disagreement resolution - -Main Classes: - - AutomatedFixer: Main automated fixing engine - - AutoMerger: Automatic merging of duplicates and conflicts - - AutoResolver: Automatic conflict and inconsistency resolution - -Example Usage: - >>> from semantica.kg_qa import AutomatedFixer - >>> fixer = AutomatedFixer() - >>> result = fixer.fix_duplicates(knowledge_graph) - >>> result = fixer.fix_missing_properties(knowledge_graph, schema) - -Author: Semantica Contributors -License: MIT -""" - -from dataclasses import dataclass -from typing import Any, Dict, List, Optional - -from ..utils.logging import get_logger -from ..utils.progress_tracker import get_progress_tracker -from .quality_metrics import QualityMetrics - - -@dataclass -class FixResult: - """ - Fix result dataclass. - - This dataclass represents the result of an automated fix operation, - containing success status, number of fixes applied, errors encountered, - and additional metadata. - - Attributes: - success: Whether the fix operation was successful - fixed_count: Number of issues fixed - errors: List of error messages encountered during fixing - metadata: Additional metadata about the fix operation - """ - - success: bool - fixed_count: int - errors: List[str] - metadata: Dict[str, Any] - - -class AutomatedFixer: - """ - Automated fixing engine. - - This class provides automated fixing capabilities for common knowledge - graph quality issues, including duplicates, inconsistencies, and - missing properties. - - Features: - - Duplicate entity fixing - - Inconsistency resolution - - Missing property completion - - Integration with quality metrics - - Example Usage: - >>> fixer = AutomatedFixer() - >>> result = fixer.fix_duplicates(knowledge_graph) - >>> if result.success: - ... print(f"Fixed {result.fixed_count} issues") - """ - - def __init__(self, **kwargs): - """ - Initialize automated fixer. - - Sets up the fixer with configuration and quality metrics calculator. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("automated_fixer") - self.config = kwargs - self.quality_metrics = QualityMetrics() - - # Initialize progress tracker - self.progress_tracker = get_progress_tracker() - - self.logger.debug("Automated fixer initialized") - - def fix_duplicates(self, knowledge_graph: Any) -> FixResult: - """ - Fix duplicate entities. - - This method identifies and fixes duplicate entities in the knowledge - graph. In practice, this would use the deduplication module to detect - and merge duplicates. - - Args: - knowledge_graph: Knowledge graph instance (object with entities - and relationships, or dict with "entities" and - "relationships" keys) - - Returns: - FixResult: Fix result containing: - - success: Whether fixing was successful - - fixed_count: Number of duplicates fixed - - errors: List of error messages - - metadata: Additional fix metadata - """ - # Track duplicate fixing - tracking_id = self.progress_tracker.start_tracking( - file=None, - module="kg_qa", - submodule="AutomatedFixer", - message="Fixing duplicate entities", - ) - - try: - self.logger.info("Fixing duplicate entities") - - self.progress_tracker.update_tracking( - tracking_id, message="Detecting duplicates..." - ) - # In practice, this would use deduplication module - # For now, return placeholder - result = FixResult(success=True, fixed_count=0, errors=[], metadata={}) - self.progress_tracker.stop_tracking( - tracking_id, - status="completed", - message=f"Fixed {result.fixed_count} duplicate(s)", - ) - return result - - except Exception as e: - self.progress_tracker.stop_tracking( - tracking_id, status="failed", message=str(e) - ) - raise - - def fix_inconsistencies(self, knowledge_graph: Any) -> FixResult: - """ - Fix inconsistencies. - - This method identifies and fixes logical inconsistencies in the - knowledge graph, such as conflicting property values or contradictory - relationships. - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - FixResult: Fix result with success status and fix count - """ - self.logger.info("Fixing inconsistencies") - - # In practice, this would resolve logical inconsistencies - return FixResult(success=True, fixed_count=0, errors=[], metadata={}) - - def fix_missing_properties( - self, knowledge_graph: Any, schema: Dict[str, Any] - ) -> FixResult: - """ - Fix missing required properties. - - This method identifies entities with missing required properties - (as defined in the schema) and attempts to fix them by adding - default values or inferring values from context. - - Args: - knowledge_graph: Knowledge graph instance - schema: Schema definition containing required property constraints - - Returns: - FixResult: Fix result with number of properties added - """ - self.logger.info("Fixing missing properties") - - fixed_count = 0 - errors = [] - - # In practice, this would: - # 1. Find entities with missing required properties - # 2. Add default values or infer values - # 3. Update the knowledge graph - - return FixResult( - success=len(errors) == 0, - fixed_count=fixed_count, - errors=errors, - metadata={}, - ) - - -class AutoMerger: - """ - Automatic merging engine. - - This class provides automatic merging capabilities for duplicate entities, - relationships, and conflicting properties in knowledge graphs. - - Features: - - Duplicate entity merging - - Duplicate relationship merging - - Conflicting property resolution - - Example Usage: - >>> merger = AutoMerger() - >>> result = merger.merge_duplicate_entities(knowledge_graph) - >>> result = merger.merge_conflicting_properties(knowledge_graph) - """ - - def __init__(self, **kwargs): - """ - Initialize auto merger. - - Sets up the merger with configuration options. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("auto_merger") - self.config = kwargs - - self.logger.debug("Auto merger initialized") - - def merge_duplicate_entities(self, knowledge_graph: Any) -> FixResult: - """ - Merge duplicate entities. - - This method identifies duplicate entities and merges them into - single entities, combining properties and updating relationships. - In practice, this would use the deduplication module. - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - FixResult: Merge result with number of entities merged - """ - self.logger.info("Merging duplicate entities") - - # In practice, this would: - # 1. Identify duplicate entities - # 2. Merge properties - # 3. Update relationships - # 4. Remove duplicates - - return FixResult(success=True, fixed_count=0, errors=[], metadata={}) - - def merge_duplicate_relationships(self, knowledge_graph: Any) -> FixResult: - """ - Merge duplicate relationships. - - This method identifies duplicate relationships (same source, target, - and type) and merges them, combining properties and metadata. - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - FixResult: Merge result with number of relationships merged - """ - self.logger.info("Merging duplicate relationships") - - return FixResult(success=True, fixed_count=0, errors=[], metadata={}) - - def merge_conflicting_properties(self, knowledge_graph: Any) -> FixResult: - """ - Merge conflicting properties. - - This method identifies entities with conflicting property values - (same property with different values) and resolves conflicts using - configurable strategies (e.g., highest confidence, most recent). - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - FixResult: Merge result with number of conflicts resolved - """ - self.logger.info("Merging conflicting properties") - - return FixResult(success=True, fixed_count=0, errors=[], metadata={}) - - -class AutoResolver: - """ - Automatic resolution engine. - - This class provides automatic resolution capabilities for conflicts, - disagreements, and inconsistencies in knowledge graphs. - - Features: - - Conflict resolution - - Disagreement resolution - - Inconsistency resolution - - Example Usage: - >>> resolver = AutoResolver() - >>> result = resolver.resolve_conflicts(knowledge_graph) - >>> result = resolver.resolve_inconsistencies(knowledge_graph) - """ - - def __init__(self, **kwargs): - """ - Initialize auto resolver. - - Sets up the resolver with configuration options. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("auto_resolver") - self.config = kwargs - - self.logger.debug("Auto resolver initialized") - - def resolve_conflicts(self, knowledge_graph: Any) -> FixResult: - """ - Resolve conflicts. - - This method identifies and resolves conflicts in the knowledge graph, - such as conflicting property values or contradictory relationships. - In practice, this would use the conflict resolution module. - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - FixResult: Resolution result with number of conflicts resolved - """ - self.logger.info("Resolving conflicts") - - return FixResult(success=True, fixed_count=0, errors=[], metadata={}) - - def resolve_disagreements(self, knowledge_graph: Any) -> FixResult: - """ - Resolve disagreements. - - This method identifies and resolves disagreements between different - sources or versions of the same information in the knowledge graph. - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - FixResult: Resolution result with number of disagreements resolved - """ - self.logger.info("Resolving disagreements") - - return FixResult(success=True, fixed_count=0, errors=[], metadata={}) - - def resolve_inconsistencies(self, knowledge_graph: Any) -> FixResult: - """ - Resolve inconsistencies. - - This method identifies and resolves logical inconsistencies in the - knowledge graph, such as circular dependencies or contradictory - hierarchical relationships. - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - FixResult: Resolution result with number of inconsistencies resolved - """ - self.logger.info("Resolving inconsistencies") - - return FixResult(success=True, fixed_count=0, errors=[], metadata={}) diff --git a/semantica/kg_qa/config.py b/semantica/kg_qa/config.py deleted file mode 100644 index 597b1cce..00000000 --- a/semantica/kg_qa/config.py +++ /dev/null @@ -1,167 +0,0 @@ -""" -Configuration Management Module for KG QA - -This module provides centralized configuration management for knowledge graph quality -assurance operations, supporting multiple configuration sources including environment -variables, config files, and programmatic configuration. - -Supported Configuration Sources: - - Environment variables: KG_QA_QUALITY_THRESHOLD, KG_QA_CONSISTENCY_THRESHOLD, etc. - - Config files: YAML, JSON, TOML formats - - Programmatic: Python API for setting QA configurations - -Algorithms Used: - - Environment Variable Parsing: OS-level environment variable access - - YAML Parsing: YAML parser for configuration file loading - - JSON Parsing: JSON parser for configuration file loading - - TOML Parsing: TOML parser for configuration file loading - - Fallback Chain: Priority-based configuration resolution - - Dictionary Merging: Deep merge algorithms for configuration updates - -Key Features: - - Environment variable support for QA parameters - - Config file support (YAML, JSON, TOML formats) - - Programmatic configuration via Python API - - Method-specific configuration management - - Automatic fallback chain (config file -> environment -> defaults) - - Global config instance for easy access - -Main Classes: - - KGQAConfig: Main configuration manager class for kg_qa module - -Example Usage: - >>> from semantica.kg_qa.config import kg_qa_config - >>> threshold = kg_qa_config.get("quality_threshold", default=0.7) - >>> kg_qa_config.set("quality_threshold", 0.8) - >>> method_config = kg_qa_config.get_method_config("assess") -""" - -import os -from pathlib import Path -from typing import Any, Dict, Optional - -from ..utils.logging import get_logger - - -class KGQAConfig: - """Configuration manager for KG QA module - supports .env files, environment variables, and programmatic config.""" - - def __init__(self, config_file: Optional[str] = None): - self.logger = get_logger("kg_qa_config") - self._configs: Dict[str, Any] = {} - self._method_configs: Dict[str, Dict] = {} - self._load_config_file(config_file) - self._load_env_vars() - - def _load_config_file(self, config_file: Optional[str]): - if config_file and Path(config_file).exists(): - try: - if config_file.endswith(".yaml") or config_file.endswith(".yml"): - import yaml - - with open(config_file, "r") as f: - data = yaml.safe_load(f) or {} - self._configs.update(data.get("kg_qa", {})) - self._method_configs.update(data.get("kg_qa_methods", {})) - elif config_file.endswith(".json"): - import json - - with open(config_file, "r") as f: - data = json.load(f) or {} - self._configs.update(data.get("kg_qa", {})) - self._method_configs.update(data.get("kg_qa_methods", {})) - elif config_file.endswith(".toml"): - import toml - - with open(config_file, "r") as f: - data = toml.load(f) or {} - if "kg_qa" in data: - self._configs.update(data["kg_qa"]) - if "kg_qa_methods" in data: - self._method_configs.update(data["kg_qa_methods"]) - self.logger.info(f"Loaded KG QA config from {config_file}") - except Exception as e: - self.logger.warning(f"Failed to load config file {config_file}: {e}") - - def _load_env_vars(self): - env_mappings = { - "KG_QA_QUALITY_THRESHOLD": ("quality_threshold", float), - "KG_QA_CONSISTENCY_THRESHOLD": ("consistency_threshold", float), - "KG_QA_COMPLETENESS_THRESHOLD": ("completeness_threshold", float), - "KG_QA_ENABLE_AUTO_FIX": ("enable_auto_fix", bool), - "KG_QA_REPORT_FORMAT": ("report_format", str), - } - - for env_key, (config_key, type_func) in env_mappings.items(): - value = os.getenv(env_key) - if value: - try: - if type_func == bool: - self._configs[config_key] = value.lower() in ( - "true", - "1", - "yes", - "on", - ) - else: - self._configs[config_key] = type_func(value) - except (ValueError, TypeError): - self.logger.warning(f"Failed to parse {env_key}={value}") - - env_prefix = "KG_QA_" - for key, value in os.environ.items(): - if key.startswith(env_prefix) and key not in env_mappings: - config_key = key[len(env_prefix) :].lower() - if value.lower() in ("true", "false"): - self._configs[config_key] = value.lower() == "true" - elif value.isdigit(): - self._configs[config_key] = int(value) - else: - try: - self._configs[config_key] = float(value) - except ValueError: - self._configs[config_key] = value - - def set(self, key: str, value: Any): - """Set configuration value programmatically.""" - self._configs[key] = value - - def get(self, key: str, default: Any = None) -> Any: - """Get configuration value with fallback chain: config -> env -> default.""" - if key in self._configs: - return self._configs[key] - - env_key = f"KG_QA_{key.upper()}" - value = os.getenv(env_key) - if value: - try: - if isinstance(default, int): - return int(value) - elif isinstance(default, float): - return float(value) - elif isinstance(default, bool): - return value.lower() in ("true", "1", "yes", "on") - return value - except (ValueError, TypeError): - pass - - return default - - def set_method_config(self, method: str, **config): - """Set method-specific configuration.""" - self._method_configs[method] = config - - def get_method_config(self, method: str) -> Dict: - """Get method-specific configuration.""" - return self._method_configs.get(method, {}) - - def get_all(self) -> Dict[str, Any]: - """Get all configuration.""" - return { - "config": self._configs.copy(), - "method_configs": self._method_configs.copy(), - } - - -# Global config instance -kg_qa_config = KGQAConfig() diff --git a/semantica/kg_qa/kg_qa_usage.md b/semantica/kg_qa/kg_qa_usage.md deleted file mode 100644 index 65191a94..00000000 --- a/semantica/kg_qa/kg_qa_usage.md +++ /dev/null @@ -1,1213 +0,0 @@ -# Knowledge Graph Quality Assurance Module Usage Guide - -This comprehensive guide demonstrates how to use the knowledge graph quality assurance module for quality assessment, validation, reporting, and automated fixing of knowledge graphs. - -## Table of Contents - -1. [Basic Usage](#basic-usage) -2. [Quality Assessment](#quality-assessment) -3. [Quality Reporting](#quality-reporting) -4. [Consistency Checking](#consistency-checking) -5. [Completeness Validation](#completeness-validation) -6. [Quality Metrics](#quality-metrics) -7. [Validation Engine](#validation-engine) -8. [Automated Fixes](#automated-fixes) -9. [Using Methods](#using-methods) -10. [Using Registry](#using-registry) -11. [Configuration](#configuration) -12. [Advanced Examples](#advanced-examples) - -## Basic Usage - -### Using the Convenience Functions - -```python -from semantica.kg_qa import assess_quality, generate_quality_report - -# Assess overall quality -knowledge_graph = { - "entities": [ - {"id": "1", "name": "Alice", "type": "Person"}, - {"id": "2", "name": "Bob", "type": "Person"} - ], - "relationships": [ - {"source": "1", "target": "2", "type": "knows"} - ] -} - -score = assess_quality(knowledge_graph, method="default") -print(f"Overall quality score: {score:.2f}") - -# Generate quality report -schema = { - "constraints": { - "Person": { - "required_props": ["id", "name", "type"] - } - } -} - -report = generate_quality_report(knowledge_graph, schema, method="default") -print(f"Overall score: {report.overall_score:.2f}") -print(f"Completeness: {report.completeness_score:.2f}") -print(f"Consistency: {report.consistency_score:.2f}") -print(f"Issues found: {len(report.issues)}") -``` - -### Using Main Classes - -```python -from semantica.kg_qa import KGQualityAssessor, QualityMetrics, ValidationEngine - -# Create quality assessor -assessor = KGQualityAssessor() - -# Assess overall quality -score = assessor.assess_overall_quality(knowledge_graph) -print(f"Quality score: {score:.2f}") - -# Generate quality report -report = assessor.generate_quality_report(knowledge_graph, schema) - -# Identify quality issues -issues = assessor.identify_quality_issues(knowledge_graph, schema) -print(f"Found {len(issues)} quality issues") -``` - -## Quality Assessment - -### Basic Quality Assessment - -```python -from semantica.kg_qa import assess_quality, KGQualityAssessor - -# Using convenience function -score = assess_quality(knowledge_graph, method="default") - -# Using class directly -assessor = KGQualityAssessor() -score = assessor.assess_overall_quality(knowledge_graph) -``` - -### Comprehensive Assessment - -```python -from semantica.kg_qa import assess_quality - -# Comprehensive assessment with all metrics -score = assess_quality( - knowledge_graph, - method="comprehensive" -) -``` - -### Quick Assessment - -```python -from semantica.kg_qa import assess_quality - -# Quick assessment with basic metrics -score = assess_quality( - knowledge_graph, - method="quick" -) -``` - -## Quality Reporting - -### Basic Report Generation - -```python -from semantica.kg_qa import generate_quality_report, QualityReporter - -# Using convenience function -report = generate_quality_report( - knowledge_graph, - schema, - method="default" -) - -print(f"Overall score: {report.overall_score:.2f}") -print(f"Completeness: {report.completeness_score:.2f}") -print(f"Consistency: {report.consistency_score:.2f}") - -# Using class directly -reporter = QualityReporter() -quality_metrics = { - "overall": 0.85, - "completeness": 0.90, - "consistency": 0.80 -} -report = reporter.generate_report(knowledge_graph, quality_metrics) -``` - -### Detailed Report - -```python -from semantica.kg_qa import generate_quality_report - -# Generate detailed report with all issues -report = generate_quality_report( - knowledge_graph, - schema, - method="detailed" -) - -# Access issues -for issue in report.issues: - print(f"Issue: {issue.id}") - print(f" Type: {issue.type}") - print(f" Severity: {issue.severity}") - print(f" Description: {issue.description}") -``` - -### Summary Report - -```python -from semantica.kg_qa import generate_quality_report - -# Generate summary report only -report = generate_quality_report( - knowledge_graph, - schema, - method="summary" -) -``` - -### Exporting Reports - -```python -from semantica.kg_qa import export_report, generate_quality_report - -# Generate report -report = generate_quality_report(knowledge_graph, schema) - -# Export to JSON -json_report = export_report(report, format="json") -print(json_report) - -# Export to YAML -yaml_report = export_report(report, format="yaml") -print(yaml_report) - -# Export using QualityReporter directly -from semantica.kg_qa import QualityReporter -reporter = QualityReporter() -json_output = reporter.export_report(report, format="json") -``` - -### Issue Identification - -```python -from semantica.kg_qa import identify_quality_issues - -# Identify all quality issues -issues = identify_quality_issues(knowledge_graph, schema, method="default") - -for issue in issues: - print(f"Issue ID: {issue['id']}") - print(f" Type: {issue['type']}") - print(f" Severity: {issue['severity']}") - print(f" Description: {issue['description']}") -``` - -### Issue Tracking - -```python -from semantica.kg_qa import IssueTracker, QualityIssue - -tracker = IssueTracker() - -# Add issues -issue = QualityIssue( - id="missing_property_1", - type="completeness", - severity="medium", - description="Entity missing required property 'email'", - entity_id="1" -) -tracker.add_issue(issue) - -# List all issues -all_issues = tracker.list_issues() -print(f"Total issues: {len(all_issues)}") - -# Filter by severity -high_issues = tracker.list_issues(severity="high") -print(f"High severity issues: {len(high_issues)}") - -# Get specific issue -issue = tracker.get_issue("missing_property_1") - -# Resolve issue -tracker.resolve_issue("missing_property_1") -``` - -## Consistency Checking - -### Logical Consistency - -```python -from semantica.kg_qa import check_consistency, ConsistencyChecker - -# Using convenience function -is_consistent = check_consistency( - knowledge_graph, - consistency_type="logical", - method="default" -) -print(f"Logically consistent: {is_consistent}") - -# Using class directly -checker = ConsistencyChecker() -is_consistent = checker.check_logical_consistency(knowledge_graph) -``` - -### Temporal Consistency - -```python -from semantica.kg_qa import check_consistency - -# Check temporal consistency -is_consistent = check_consistency( - knowledge_graph, - consistency_type="temporal", - method="default" -) -print(f"Temporally consistent: {is_consistent}") -``` - -### Hierarchical Consistency - -```python -from semantica.kg_qa import check_consistency - -# Check hierarchical consistency -is_consistent = check_consistency( - knowledge_graph, - consistency_type="hierarchical", - method="default" -) -print(f"Hierarchically consistent: {is_consistent}") -``` - -### All Consistency Checks - -```python -from semantica.kg_qa import check_consistency - -# Check all consistency types -consistency_results = check_consistency( - knowledge_graph, - consistency_type="all", - method="default" -) - -print(f"Logical: {consistency_results['logical']}") -print(f"Temporal: {consistency_results['temporal']}") -print(f"Hierarchical: {consistency_results['hierarchical']}") -``` - -### Using Consistency Metrics - -```python -from semantica.kg_qa import ConsistencyMetrics - -metrics = ConsistencyMetrics() - -# Calculate logical consistency score -logical_score = metrics.calculate_logical_consistency(knowledge_graph) -print(f"Logical consistency score: {logical_score:.2f}") - -# Calculate temporal consistency score -temporal_score = metrics.calculate_temporal_consistency(knowledge_graph) -print(f"Temporal consistency score: {temporal_score:.2f}") - -# Calculate hierarchical consistency score -hierarchical_score = metrics.calculate_hierarchical_consistency(knowledge_graph) -print(f"Hierarchical consistency score: {hierarchical_score:.2f}") -``` - -## Completeness Validation - -### Entity Completeness - -```python -from semantica.kg_qa import validate_completeness, CompletenessValidator - -entities = [ - {"id": "1", "name": "Alice", "type": "Person"}, - {"id": "2", "name": "Bob", "type": "Person"} -] - -schema = { - "constraints": { - "Person": { - "required_props": ["id", "name", "type", "email"] - } - } -} - -# Using convenience function -is_complete = validate_completeness( - entities=entities, - schema=schema, - completeness_type="entity", - method="default" -) -print(f"Entities complete: {is_complete}") - -# Using class directly -validator = CompletenessValidator() -is_complete = validator.validate_entity_completeness(entities, schema) -``` - -### Relationship Completeness - -```python -from semantica.kg_qa import validate_completeness - -relationships = [ - {"source": "1", "target": "2", "type": "knows"}, - {"source": "2", "target": "1", "type": "knows"} -] - -# Validate relationship completeness -is_complete = validate_completeness( - relationships=relationships, - schema=schema, - completeness_type="relationship", - method="default" -) -print(f"Relationships complete: {is_complete}") -``` - -### Property Completeness - -```python -from semantica.kg_qa import validate_completeness - -properties = { - "Person": { - "id": True, - "name": True, - "type": True, - "email": False # Missing - } -} - -# Validate property completeness -is_complete = validate_completeness( - properties=properties, - schema=schema, - completeness_type="property", - method="default" -) -print(f"Properties complete: {is_complete}") -``` - -### All Completeness Checks - -```python -from semantica.kg_qa import validate_completeness - -# Validate all completeness types -completeness_results = validate_completeness( - entities=entities, - relationships=relationships, - properties=properties, - schema=schema, - completeness_type="all", - method="default" -) - -print(f"Entity completeness: {completeness_results['entity']}") -print(f"Relationship completeness: {completeness_results['relationship']}") -print(f"Property completeness: {completeness_results['property']}") -``` - -### Using Completeness Metrics - -```python -from semantica.kg_qa import CompletenessMetrics - -metrics = CompletenessMetrics() - -# Calculate entity completeness score -entity_score = metrics.calculate_entity_completeness(entities, schema) -print(f"Entity completeness score: {entity_score:.2f}") - -# Calculate relationship completeness score -rel_score = metrics.calculate_relationship_completeness(relationships, schema) -print(f"Relationship completeness score: {rel_score:.2f}") - -# Calculate property completeness score -prop_score = metrics.calculate_property_completeness(properties, schema) -print(f"Property completeness score: {prop_score:.2f}") -``` - -## Quality Metrics - -### Overall Quality Score - -```python -from semantica.kg_qa import calculate_quality_metrics, QualityMetrics - -# Using convenience function -overall_score = calculate_quality_metrics( - knowledge_graph, - metrics_type="overall", - method="default" -) -print(f"Overall quality score: {overall_score:.2f}") - -# Using class directly -metrics = QualityMetrics() -overall_score = metrics.calculate_overall_score(knowledge_graph) -``` - -### Entity Quality Score - -```python -from semantica.kg_qa import calculate_quality_metrics - -entities = [ - {"id": "1", "name": "Alice", "type": "Person"}, - {"id": "2", "name": "Bob", "type": "Person"} -] - -# Calculate entity quality -entity_score = calculate_quality_metrics( - {"entities": entities}, - metrics_type="entity", - method="default" -) -print(f"Entity quality score: {entity_score:.2f}") -``` - -### Relationship Quality Score - -```python -from semantica.kg_qa import calculate_quality_metrics - -relationships = [ - {"source": "1", "target": "2", "type": "knows"} -] - -# Calculate relationship quality -rel_score = calculate_quality_metrics( - {"relationships": relationships}, - metrics_type="relationship", - method="default" -) -print(f"Relationship quality score: {rel_score:.2f}") -``` - -### All Quality Metrics - -```python -from semantica.kg_qa import calculate_quality_metrics - -# Calculate all metrics -all_metrics = calculate_quality_metrics( - knowledge_graph, - metrics_type="all", - method="default" -) - -print(f"Overall: {all_metrics['overall']:.2f}") -print(f"Entity: {all_metrics['entity']:.2f}") -print(f"Relationship: {all_metrics['relationship']:.2f}") -``` - -### Using Quality Metrics Directly - -```python -from semantica.kg_qa import QualityMetrics - -metrics = QualityMetrics() - -# Calculate overall score -overall = metrics.calculate_overall_score(knowledge_graph) - -# Calculate entity quality -entities = getattr(knowledge_graph, "entities", []) -entity_quality = metrics.calculate_entity_quality(entities) - -# Calculate relationship quality -relationships = getattr(knowledge_graph, "relationships", []) -rel_quality = metrics.calculate_relationship_quality(relationships) - -print(f"Overall: {overall:.2f}") -print(f"Entity: {entity_quality:.2f}") -print(f"Relationship: {rel_quality:.2f}") -``` - -## Validation Engine - -### Basic Validation - -```python -from semantica.kg_qa import validate_graph, ValidationEngine - -# Using convenience function -result = validate_graph(knowledge_graph, method="default") - -if result.valid: - print("Graph is valid") -else: - print(f"Validation failed with {len(result.errors)} errors") - for error in result.errors: - print(f" Error: {error}") - -# Using class directly -engine = ValidationEngine() -result = engine.validate(knowledge_graph) -``` - -### Custom Rule Validation - -```python -from semantica.kg_qa import validate_graph, ValidationEngine - -# Define custom validation rule -def check_entity_ids(knowledge_graph): - """Check that all entities have valid IDs.""" - entities = getattr(knowledge_graph, "entities", []) - errors = [] - for entity in entities: - if "id" not in entity or not entity["id"]: - errors.append(f"Entity missing ID: {entity}") - return {"error": errors[0] if errors else None} - -# Validate with custom rule -result = validate_graph( - knowledge_graph, - rules=[check_entity_ids], - method="custom" -) - -# Using class directly -engine = ValidationEngine() -engine.add_rule(check_entity_ids) -result = engine.validate(knowledge_graph) -``` - -### Constraint-Based Validation - -```python -from semantica.kg_qa import validate_graph, ConstraintValidator - -constraints = { - "entities": { - "Person": { - "required_props": ["id", "name", "type"] - } - }, - "relationships": { - "knows": { - "domain": "Person", - "range": "Person" - } - } -} - -# Validate with constraints -validator = ConstraintValidator() -result = validator.validate_constraints(knowledge_graph, constraints) - -if result.valid: - print("Graph satisfies all constraints") -else: - print(f"Constraint violations: {len(result.errors)}") -``` - -### Rule Management - -```python -from semantica.kg_qa import ValidationEngine - -engine = ValidationEngine() - -# Add validation rule -def check_required_fields(knowledge_graph): - """Check required fields.""" - # Validation logic - return {"error": None} - -engine.add_rule(check_required_fields) - -# Validate with stored rules -result = engine.validate(knowledge_graph) - -# Remove rule -engine.remove_rule(check_required_fields) -``` - -### Rule Validator - -```python -from semantica.kg_qa import RuleValidator - -validator = RuleValidator() - -# Validate against specific rule -result = validator.validate_rule(knowledge_graph, "entity_id_rule") - -# Validate against multiple rules -results = validator.validate_all_rules( - knowledge_graph, - ["rule1", "rule2", "rule3"] -) - -for rule_name, result in results.items(): - print(f"{rule_name}: {'Valid' if result.valid else 'Invalid'}") -``` - -## Automated Fixes - -### Fix Duplicates - -```python -from semantica.kg_qa import fix_issues, AutomatedFixer - -# Using convenience function -result = fix_issues( - knowledge_graph, - fix_type="duplicates", - method="default" -) - -if result.success: - print(f"Fixed {result.fixed_count} duplicate(s)") -else: - print(f"Fix failed: {result.errors}") - -# Using class directly -fixer = AutomatedFixer() -result = fixer.fix_duplicates(knowledge_graph) -``` - -### Fix Inconsistencies - -```python -from semantica.kg_qa import fix_issues - -# Fix logical inconsistencies -result = fix_issues( - knowledge_graph, - fix_type="inconsistencies", - method="default" -) - -print(f"Fixed {result.fixed_count} inconsistency(ies)") -``` - -### Fix Missing Properties - -```python -from semantica.kg_qa import fix_issues - -schema = { - "constraints": { - "Person": { - "required_props": ["id", "name", "type", "email"] - } - } -} - -# Fix missing required properties -result = fix_issues( - knowledge_graph, - fix_type="missing_properties", - schema=schema, - method="default" -) - -print(f"Added {result.fixed_count} missing property(ies)") -``` - -### Apply All Fixes - -```python -from semantica.kg_qa import fix_issues - -# Apply all automated fixes -result = fix_issues( - knowledge_graph, - fix_type="all", - schema=schema, - method="default" -) - -print(f"Total fixes applied: {result.fixed_count}") -print(f"Fixes: {result.metadata.get('fixes_applied', [])}") -``` - -### Using Auto Merger - -```python -from semantica.kg_qa import AutoMerger - -merger = AutoMerger() - -# Merge duplicate entities -result = merger.merge_duplicate_entities(knowledge_graph) -print(f"Merged {result.fixed_count} duplicate entity(ies)") - -# Merge duplicate relationships -result = merger.merge_duplicate_relationships(knowledge_graph) -print(f"Merged {result.fixed_count} duplicate relationship(s)") - -# Merge conflicting properties -result = merger.merge_conflicting_properties(knowledge_graph) -print(f"Resolved {result.fixed_count} property conflict(s)") -``` - -### Using Auto Resolver - -```python -from semantica.kg_qa import AutoResolver - -resolver = AutoResolver() - -# Resolve conflicts -result = resolver.resolve_conflicts(knowledge_graph) -print(f"Resolved {result.fixed_count} conflict(s)") - -# Resolve disagreements -result = resolver.resolve_disagreements(knowledge_graph) -print(f"Resolved {result.fixed_count} disagreement(s)") - -# Resolve inconsistencies -result = resolver.resolve_inconsistencies(knowledge_graph) -print(f"Resolved {result.fixed_count} inconsistency(ies)") -``` - -## Using Methods - -### Getting Available Methods - -```python -from semantica.kg_qa.methods import get_qa_method, list_available_methods - -# List all available methods -all_methods = list_available_methods() -print("Available methods:", all_methods) - -# List methods for specific task -assess_methods = list_available_methods("assess") -print("Assessment methods:", assess_methods) - -# Get specific method -assess_method = get_qa_method("assess", "default") -if assess_method: - score = assess_method(knowledge_graph) -``` - -### Method Examples - -```python -from semantica.kg_qa.methods import ( - assess_quality, - generate_quality_report, - identify_quality_issues, - check_consistency, - validate_completeness, - calculate_quality_metrics, - validate_graph, - export_report, - fix_issues -) - -# Quality assessment -score = assess_quality(knowledge_graph, method="default") - -# Report generation -report = generate_quality_report(knowledge_graph, schema, method="default") - -# Issue identification -issues = identify_quality_issues(knowledge_graph, schema, method="default") - -# Consistency checking -is_consistent = check_consistency(knowledge_graph, consistency_type="logical") - -# Completeness validation -is_complete = validate_completeness(entities, schema, completeness_type="entity") - -# Quality metrics -metrics = calculate_quality_metrics(knowledge_graph, metrics_type="overall") - -# Graph validation -result = validate_graph(knowledge_graph, method="default") - -# Report export -json_report = export_report(report, format="json") - -# Automated fixes -fix_result = fix_issues(knowledge_graph, fix_type="duplicates") -``` - -## Using Registry - -### Registering Custom Methods - -```python -from semantica.kg_qa.registry import method_registry - -# Custom quality assessment method -def custom_assessment(knowledge_graph, **kwargs): - """Custom assessment logic.""" - # Your custom assessment code - score = 0.85 # Calculate score - return score - -# Register custom method -method_registry.register("assess", "custom_assessment", custom_assessment) - -# Use custom method -from semantica.kg_qa.methods import get_qa_method -custom_method = get_qa_method("assess", "custom_assessment") -score = custom_method(knowledge_graph) -``` - -### Listing Registered Methods - -```python -from semantica.kg_qa.registry import method_registry - -# List all registered methods -all_methods = method_registry.list_all() -print("Registered methods:", all_methods) - -# List methods for specific task -assess_methods = method_registry.list_all("assess") -print("Assessment methods:", assess_methods) - -consistency_methods = method_registry.list_all("consistency") -print("Consistency methods:", consistency_methods) -``` - -### Unregistering Methods - -```python -from semantica.kg_qa.registry import method_registry - -# Unregister a method -method_registry.unregister("assess", "custom_assessment") - -# Clear all methods for a task -method_registry.clear("assess") - -# Clear all methods -method_registry.clear() -``` - -## Configuration - -### Using Configuration Manager - -```python -from semantica.kg_qa.config import kg_qa_config - -# Get configuration values -quality_threshold = kg_qa_config.get("quality_threshold", default=0.7) -consistency_threshold = kg_qa_config.get("consistency_threshold", default=0.8) -completeness_threshold = kg_qa_config.get("completeness_threshold", default=0.8) - -# Set configuration values -kg_qa_config.set("quality_threshold", 0.75) -kg_qa_config.set("consistency_threshold", 0.85) - -# Method-specific configuration -kg_qa_config.set_method_config("assess", quality_threshold=0.8) -assess_config = kg_qa_config.get_method_config("assess") - -# Get all configuration -all_config = kg_qa_config.get_all() -print("All config:", all_config) -``` - -### Environment Variables - -```bash -# Set environment variables -export KG_QA_QUALITY_THRESHOLD=0.75 -export KG_QA_CONSISTENCY_THRESHOLD=0.85 -export KG_QA_COMPLETENESS_THRESHOLD=0.80 -export KG_QA_ENABLE_AUTO_FIX=true -export KG_QA_REPORT_FORMAT=json -``` - -### Configuration File - -```yaml -# config.yaml -kg_qa: - quality_threshold: 0.75 - consistency_threshold: 0.85 - completeness_threshold: 0.80 - enable_auto_fix: true - report_format: json - -kg_qa_methods: - assess: - quality_threshold: 0.8 - report: - format: json - consistency: - threshold: 0.85 -``` - -```python -from semantica.kg_qa.config import KGQAConfig - -# Load from config file -config = KGQAConfig(config_file="config.yaml") -quality_threshold = config.get("quality_threshold") -``` - -## Advanced Examples - -### Complete Quality Assurance Pipeline - -```python -from semantica.kg_qa import ( - assess_quality, - generate_quality_report, - check_consistency, - validate_completeness, - fix_issues -) - -# Step 1: Assess overall quality -score = assess_quality(knowledge_graph, method="default") -print(f"Initial quality score: {score:.2f}") - -# Step 2: Generate comprehensive report -report = generate_quality_report(knowledge_graph, schema, method="detailed") -print(f"Report generated with {len(report.issues)} issues") - -# Step 3: Check consistency -consistency_results = check_consistency(knowledge_graph, consistency_type="all") -print(f"Consistency checks: {consistency_results}") - -# Step 4: Validate completeness -completeness_results = validate_completeness( - entities=entities, - relationships=relationships, - schema=schema, - completeness_type="all" -) -print(f"Completeness checks: {completeness_results}") - -# Step 5: Apply automated fixes -fix_result = fix_issues(knowledge_graph, fix_type="all", schema=schema) -print(f"Fixed {fix_result.fixed_count} issue(s)") - -# Step 6: Re-assess quality -final_score = assess_quality(knowledge_graph, method="default") -print(f"Final quality score: {final_score:.2f}") -print(f"Improvement: {final_score - score:.2f}") -``` - -### Custom Validation Rules - -```python -from semantica.kg_qa import ValidationEngine - -engine = ValidationEngine() - -# Rule 1: Check entity IDs -def validate_entity_ids(kg): - entities = getattr(kg, "entities", []) - errors = [] - for entity in entities: - if "id" not in entity: - errors.append(f"Entity missing ID: {entity}") - return {"error": errors[0] if errors else None} - -# Rule 2: Check relationship references -def validate_relationship_refs(kg): - entities = getattr(kg, "entities", []) - relationships = getattr(kg, "relationships", []) - entity_ids = {e.get("id") for e in entities} - errors = [] - for rel in relationships: - source = rel.get("source") or rel.get("subject") - target = rel.get("target") or rel.get("object") - if source not in entity_ids: - errors.append(f"Relationship references unknown source: {source}") - if target not in entity_ids: - errors.append(f"Relationship references unknown target: {target}") - return {"error": errors[0] if errors else None} - -# Add rules -engine.add_rule(validate_entity_ids) -engine.add_rule(validate_relationship_refs) - -# Validate -result = engine.validate(knowledge_graph) -if not result.valid: - print(f"Validation failed: {result.errors}") -``` - -### Quality Monitoring Workflow - -```python -from semantica.kg_qa import ( - KGQualityAssessor, - IssueTracker, - QualityReporter -) - -assessor = KGQualityAssessor() -tracker = IssueTracker() -reporter = QualityReporter() - -# Assess quality -score = assessor.assess_overall_quality(knowledge_graph) - -# Generate report -report = assessor.generate_quality_report(knowledge_graph, schema) - -# Track issues -for issue in report.issues: - tracker.add_issue(issue) - -# Monitor high-severity issues -high_issues = tracker.list_issues(severity="high") -print(f"High severity issues: {len(high_issues)}") - -# Export report for monitoring -json_report = reporter.export_report(report, format="json") -# Save to file or send to monitoring system -``` - -### Batch Quality Assessment - -```python -from semantica.kg_qa import assess_quality, generate_quality_report - -knowledge_graphs = [ - {"entities": [...], "relationships": [...]}, - {"entities": [...], "relationships": [...]}, - {"entities": [...], "relationships": [...]} -] - -# Assess multiple graphs -results = [] -for kg in knowledge_graphs: - score = assess_quality(kg, method="default") - report = generate_quality_report(kg, schema, method="default") - results.append({ - "score": score, - "issues": len(report.issues), - "completeness": report.completeness_score, - "consistency": report.consistency_score - }) - -# Analyze results -avg_score = sum(r["score"] for r in results) / len(results) -total_issues = sum(r["issues"] for r in results) -print(f"Average quality score: {avg_score:.2f}") -print(f"Total issues across graphs: {total_issues}") -``` - -### Integration with KG Module - -```python -from semantica.kg import build -from semantica.kg_qa import assess_quality, generate_quality_report, fix_issues - -# Build knowledge graph -kg = build(sources, merge_entities=True, resolve_conflicts=True) - -# Assess quality -score = assess_quality(kg, method="default") -print(f"Quality after building: {score:.2f}") - -# Generate report -schema = { - "constraints": { - "Person": {"required_props": ["id", "name", "type"]} - } -} -report = generate_quality_report(kg, schema, method="default") - -# Fix issues if needed -if score < 0.7: - fix_result = fix_issues(kg, fix_type="all", schema=schema) - print(f"Fixed {fix_result.fixed_count} issue(s)") - - # Re-assess - final_score = assess_quality(kg, method="default") - print(f"Quality after fixes: {final_score:.2f}") -``` - -### Custom Quality Metrics - -```python -from semantica.kg_qa import QualityMetrics, CompletenessMetrics, ConsistencyMetrics - -# Calculate individual metrics -quality_metrics = QualityMetrics() -completeness_metrics = CompletenessMetrics() -consistency_metrics = ConsistencyMetrics() - -# Overall quality -overall = quality_metrics.calculate_overall_score(knowledge_graph) - -# Entity quality -entities = getattr(knowledge_graph, "entities", []) -entity_quality = quality_metrics.calculate_entity_quality(entities) - -# Relationship quality -relationships = getattr(knowledge_graph, "relationships", []) -rel_quality = quality_metrics.calculate_relationship_quality(relationships) - -# Completeness -entity_completeness = completeness_metrics.calculate_entity_completeness(entities, schema) - -# Consistency -logical_consistency = consistency_metrics.calculate_logical_consistency(knowledge_graph) -temporal_consistency = consistency_metrics.calculate_temporal_consistency(knowledge_graph) -hierarchical_consistency = consistency_metrics.calculate_hierarchical_consistency(knowledge_graph) - -print(f"Overall: {overall:.2f}") -print(f"Entity Quality: {entity_quality:.2f}") -print(f"Relationship Quality: {rel_quality:.2f}") -print(f"Entity Completeness: {entity_completeness:.2f}") -print(f"Logical Consistency: {logical_consistency:.2f}") -``` - -### Improvement Suggestions - -```python -from semantica.kg_qa import ImprovementSuggestions, generate_quality_report - -# Generate report -report = generate_quality_report(knowledge_graph, schema, method="default") - -# Generate improvement suggestions -suggestions_gen = ImprovementSuggestions() -suggestions = suggestions_gen.generate_suggestions(report) - -print("Improvement Suggestions:") -for suggestion in suggestions: - print(f" - {suggestion}") -``` - -## Best Practices - -1. **Regular Quality Assessment**: Assess quality regularly during knowledge graph construction and updates -2. **Schema-Based Validation**: Always provide schema for completeness validation -3. **Threshold Configuration**: Set appropriate thresholds based on your use case -4. **Issue Tracking**: Use IssueTracker to monitor and resolve quality issues over time -5. **Automated Fixes**: Use automated fixes for common issues, but review results -6. **Custom Rules**: Create custom validation rules for domain-specific requirements -7. **Report Export**: Export reports regularly for quality monitoring and auditing -8. **Integration**: Integrate QA checks into your KG construction pipeline -9. **Method Registry**: Register custom methods for domain-specific quality assessment -10. **Configuration Management**: Use configuration files for consistent QA settings across environments - diff --git a/semantica/kg_qa/kg_quality_assessor.py b/semantica/kg_qa/kg_quality_assessor.py deleted file mode 100644 index 41b24216..00000000 --- a/semantica/kg_qa/kg_quality_assessor.py +++ /dev/null @@ -1,421 +0,0 @@ -""" -KG Quality Assessor Module - -This module provides the main quality assessment coordination for the Semantica -framework, integrating all quality assurance components to provide comprehensive -quality assessment and reporting. - -Key Features: - - Overall quality assessment - - Quality report generation - - Quality issue identification - - Consistency checking - - Completeness validation - -Main Classes: - - KGQualityAssessor: Main quality assessment coordinator - - ConsistencyChecker: Consistency validation engine - - CompletenessValidator: Completeness validation engine - -Example Usage: - >>> from semantica.kg_qa import KGQualityAssessor - >>> assessor = KGQualityAssessor() - >>> score = assessor.assess_overall_quality(knowledge_graph) - >>> report = assessor.generate_quality_report(knowledge_graph, schema) - >>> issues = assessor.identify_quality_issues(knowledge_graph, schema) - -Author: Semantica Contributors -License: MIT -""" - -from typing import Any, Dict, List, Optional - -from ..utils.logging import get_logger -from ..utils.progress_tracker import get_progress_tracker -from .quality_metrics import CompletenessMetrics, ConsistencyMetrics, QualityMetrics -from .reporting import QualityReport, QualityReporter -from .validation_engine import ValidationEngine - - -class KGQualityAssessor: - """ - Knowledge Graph Quality Assessor. - - This class serves as the main coordinator for knowledge graph quality - assessment, integrating quality metrics, validation, and reporting - components to provide comprehensive quality analysis. - - Features: - - Overall quality score calculation - - Comprehensive quality report generation - - Quality issue identification - - Integration with all QA components - - Example Usage: - >>> assessor = KGQualityAssessor() - >>> score = assessor.assess_overall_quality(knowledge_graph) - >>> report = assessor.generate_quality_report(knowledge_graph, schema) - >>> issues = assessor.identify_quality_issues(knowledge_graph, schema) - """ - - def __init__(self, **kwargs): - """ - Initialize KG quality assessor. - - Sets up the assessor with all quality assurance components including - quality metrics, completeness metrics, consistency metrics, validation - engine, and quality reporter. - - Args: - **kwargs: Configuration options passed to all components - """ - self.logger = get_logger("kg_quality_assessor") - self.config = kwargs - - # Initialize components - self.quality_metrics = QualityMetrics(**kwargs) - self.completeness_metrics = CompletenessMetrics(**kwargs) - self.consistency_metrics = ConsistencyMetrics(**kwargs) - self.validation_engine = ValidationEngine(**kwargs) - self.quality_reporter = QualityReporter(**kwargs) - - # Initialize progress tracker - self.progress_tracker = get_progress_tracker() - - self.logger.debug("KG quality assessor initialized") - - def assess_overall_quality(self, knowledge_graph: Any) -> float: - """ - Assess overall quality of knowledge graph. - - This method calculates an overall quality score for the knowledge - graph by aggregating various quality metrics (completeness, consistency, - etc.) into a single score. - - Args: - knowledge_graph: Knowledge graph instance (object with entities - and relationships, or dict with "entities" and - "relationships" keys) - - Returns: - float: Overall quality score between 0.0 and 1.0 (higher is better) - """ - # Track quality assessment - tracking_id = self.progress_tracker.start_tracking( - file=None, - module="kg_qa", - submodule="KGQualityAssessor", - message="Assessing overall quality", - ) - - try: - self.logger.info("Assessing overall quality") - - self.progress_tracker.update_tracking( - tracking_id, message="Calculating quality metrics..." - ) - # Calculate metrics - overall_score = self.quality_metrics.calculate_overall_score( - knowledge_graph - ) - - self.progress_tracker.stop_tracking( - tracking_id, - status="completed", - message=f"Overall quality score: {overall_score:.2f}", - ) - return overall_score - - except Exception as e: - self.progress_tracker.stop_tracking( - tracking_id, status="failed", message=str(e) - ) - raise - - def generate_quality_report( - self, knowledge_graph: Any, schema: Optional[Dict[str, Any]] = None - ) -> QualityReport: - """ - Generate comprehensive quality report. - - This method generates a comprehensive quality report including overall - quality score, completeness score, consistency score, identified issues, - and improvement recommendations. - - Args: - knowledge_graph: Knowledge graph instance - schema: Optional schema definition for validation (if provided, - enables completeness checking against schema constraints) - - Returns: - QualityReport: Comprehensive quality report containing: - - timestamp: Report generation timestamp - - overall_score: Overall quality score - - completeness_score: Completeness score - - consistency_score: Consistency score - - issues: List of identified quality issues - - recommendations: List of improvement recommendations - - metadata: Additional report metadata - """ - self.logger.info("Generating quality report") - - # Calculate metrics - overall_score = self.quality_metrics.calculate_overall_score(knowledge_graph) - - # Get entities and relationships (simplified - in practice would query graph) - entities = getattr(knowledge_graph, "entities", []) - relationships = getattr(knowledge_graph, "relationships", []) - - completeness_score = 0.0 - if schema and entities: - completeness_score = ( - self.completeness_metrics.calculate_entity_completeness( - entities, schema - ) - ) - - consistency_score = self.consistency_metrics.calculate_logical_consistency( - knowledge_graph - ) - - quality_metrics = { - "overall": overall_score, - "completeness": completeness_score, - "consistency": consistency_score, - } - - # Generate report - report = self.quality_reporter.generate_report(knowledge_graph, quality_metrics) - - return report - - def identify_quality_issues( - self, knowledge_graph: Any, schema: Optional[Dict[str, Any]] = None - ) -> List[Dict[str, Any]]: - """ - Identify quality issues in knowledge graph. - - This method identifies and returns all quality issues found in the - knowledge graph, including completeness issues, consistency issues, - and other quality problems. - - Args: - knowledge_graph: Knowledge graph instance - schema: Optional schema for validation - - Returns: - list: List of quality issue dictionaries, each containing: - - id: Issue identifier - - type: Issue type (e.g., "completeness", "consistency") - - severity: Issue severity ("low", "medium", "high") - - description: Issue description - - entity_id: Related entity ID (if applicable) - - relationship_id: Related relationship ID (if applicable) - """ - self.logger.info("Identifying quality issues") - - # Generate report to get issues - report = self.generate_quality_report(knowledge_graph, schema) - - # Convert issues to dictionaries - issues = [ - { - "id": issue.id, - "type": issue.type, - "severity": issue.severity, - "description": issue.description, - "entity_id": issue.entity_id, - "relationship_id": issue.relationship_id, - } - for issue in report.issues - ] - - return issues - - -class ConsistencyChecker: - """ - Consistency checking engine. - - This class provides consistency checking capabilities for knowledge graphs, - validating logical, temporal, and hierarchical consistency. - - Features: - - Logical consistency checking - - Temporal consistency checking - - Hierarchical consistency checking - - Example Usage: - >>> checker = ConsistencyChecker() - >>> is_logical = checker.check_logical_consistency(knowledge_graph) - >>> is_temporal = checker.check_temporal_consistency(knowledge_graph) - """ - - def __init__(self, **kwargs): - """ - Initialize consistency checker. - - Sets up the checker with consistency metrics calculator. - - Args: - **kwargs: Configuration options passed to ConsistencyMetrics - """ - self.logger = get_logger("consistency_checker") - self.consistency_metrics = ConsistencyMetrics(**kwargs) - self.config = kwargs - - self.logger.debug("Consistency checker initialized") - - def check_logical_consistency(self, knowledge_graph: Any) -> bool: - """ - Check logical consistency. - - This method checks for logical inconsistencies in the knowledge graph, - such as contradictory relationships or conflicting property values. - Returns True if the consistency score is above the threshold (0.8). - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - bool: True if logically consistent (score >= 0.8), False otherwise - """ - score = self.consistency_metrics.calculate_logical_consistency(knowledge_graph) - return score >= 0.8 - - def check_temporal_consistency(self, knowledge_graph: Any) -> bool: - """ - Check temporal consistency. - - This method checks for temporal inconsistencies in the knowledge graph, - such as relationships with invalid time ranges or temporal contradictions. - Returns True if the consistency score is above the threshold (0.8). - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - bool: True if temporally consistent (score >= 0.8), False otherwise - """ - score = self.consistency_metrics.calculate_temporal_consistency(knowledge_graph) - return score >= 0.8 - - def check_hierarchical_consistency(self, knowledge_graph: Any) -> bool: - """ - Check hierarchical consistency. - - This method checks for hierarchical inconsistencies in the knowledge - graph, such as circular inheritance or invalid parent-child relationships. - Returns True if the consistency score is above the threshold (0.8). - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - bool: True if hierarchically consistent (score >= 0.8), False otherwise - """ - score = self.consistency_metrics.calculate_hierarchical_consistency( - knowledge_graph - ) - return score >= 0.8 - - -class CompletenessValidator: - """ - Completeness validation engine. - - This class provides completeness validation capabilities for knowledge graphs, - checking whether entities, relationships, and properties meet schema - requirements. - - Features: - - Entity completeness validation - - Relationship completeness validation - - Property completeness validation - - Example Usage: - >>> validator = CompletenessValidator() - >>> is_complete = validator.validate_entity_completeness(entities, schema) - >>> is_rel_complete = validator.validate_relationship_completeness(relationships, schema) - """ - - def __init__(self, **kwargs): - """ - Initialize completeness validator. - - Sets up the validator with completeness metrics calculator. - - Args: - **kwargs: Configuration options passed to CompletenessMetrics - """ - self.logger = get_logger("completeness_validator") - self.completeness_metrics = CompletenessMetrics(**kwargs) - self.config = kwargs - - self.logger.debug("Completeness validator initialized") - - def validate_entity_completeness( - self, entities: List[Dict[str, Any]], schema: Dict[str, Any] - ) -> bool: - """ - Validate entity completeness. - - This method validates whether entities have all required properties - as defined in the schema. Returns True if the completeness score - is above the threshold (0.8). - - Args: - entities: List of entity dictionaries - schema: Schema definition containing required property constraints - - Returns: - bool: True if entities are complete (score >= 0.8), False otherwise - """ - score = self.completeness_metrics.calculate_entity_completeness( - entities, schema - ) - return score >= 0.8 - - def validate_relationship_completeness( - self, relationships: List[Dict[str, Any]], schema: Dict[str, Any] - ) -> bool: - """ - Validate relationship completeness. - - This method validates whether relationships have all required properties - as defined in the schema. Returns True if the completeness score - is above the threshold (0.8). - - Args: - relationships: List of relationship dictionaries - schema: Schema definition containing relationship constraints - - Returns: - bool: True if relationships are complete (score >= 0.8), False otherwise - """ - score = self.completeness_metrics.calculate_relationship_completeness( - relationships, schema - ) - return score >= 0.8 - - def validate_property_completeness( - self, properties: Dict[str, Any], schema: Dict[str, Any] - ) -> bool: - """ - Validate property completeness. - - This method validates whether properties meet schema requirements - for completeness. Returns True if the completeness score is above - the threshold (0.8). - - Args: - properties: Properties dictionary (mapping entity types to property dicts) - schema: Schema definition containing property constraints - - Returns: - bool: True if properties are complete (score >= 0.8), False otherwise - """ - score = self.completeness_metrics.calculate_property_completeness( - properties, schema - ) - return score >= 0.8 diff --git a/semantica/kg_qa/methods.py b/semantica/kg_qa/methods.py deleted file mode 100644 index 1b1193af..00000000 --- a/semantica/kg_qa/methods.py +++ /dev/null @@ -1,747 +0,0 @@ -""" -Knowledge Graph Quality Assurance Methods Module - -This module provides all KG QA methods as simple, reusable functions for -quality assessment, validation, reporting, and automated fixing. It supports -multiple approaches and integrates with the method registry for extensibility. - -Supported Methods: - -Quality Assessment: - - "default": Default quality assessment using KGQualityAssessor - - "comprehensive": Comprehensive assessment with all metrics - - "quick": Quick assessment with basic metrics - -Quality Reporting: - - "default": Default report generation - - "detailed": Detailed report with all issues - - "summary": Summary report only - -Consistency Checking: - - "logical": Logical consistency checking - - "temporal": Temporal consistency checking - - "hierarchical": Hierarchical consistency checking - - "all": All consistency checks - -Completeness Validation: - - "entity": Entity completeness validation - - "relationship": Relationship completeness validation - - "property": Property completeness validation - - "all": All completeness checks - -Quality Metrics: - - "overall": Overall quality score - - "entity": Entity quality score - - "relationship": Relationship quality score - - "completeness": Completeness metrics - - "consistency": Consistency metrics - -Validation: - - "default": Default validation with stored rules - - "custom": Custom rule validation - - "constraints": Constraint-based validation - -Automated Fixes: - - "duplicates": Fix duplicate entities - - "inconsistencies": Fix inconsistencies - - "missing_properties": Fix missing properties - - "all": Apply all fixes - -Algorithms Used: - -Quality Metrics Calculation: - - Weighted Averaging: Overall quality score aggregation using weighted average formula: overall = (0.6 * completeness) + (0.4 * consistency) - - Entity Quality Scoring: Required field presence checking (ID/URI, type), binary scoring (0.5 per field), average calculation across entities - - Relationship Quality Scoring: Required field presence checking (source/subject, target/object, type/predicate), weighted scoring (0.33 per field), average calculation across relationships - - Score Normalization: Min-max normalization with clamping to 0.0-1.0 range - -Completeness Metrics: - - Entity Completeness Calculation: Schema-based required property validation, ratio calculation present_props / required_props, average completeness across entities - - Relationship Completeness Calculation: Required field validation (source, target, type), completeness ratio calculation, average across relationships - - Property Completeness Calculation: Schema-based property validation per entity type, completeness ratio calculation, average across entity types - -Consistency Metrics: - - Logical Consistency Checking: Contradiction detection, conflicting relationship identification, inconsistent property value detection - - Temporal Consistency Checking: Temporal contradiction detection, invalid time range validation, conflicting temporal relationship identification - - Hierarchical Consistency Checking: Circular inheritance detection (DFS-based cycle detection), invalid parent-child relationship validation - -Validation Engine: - - Rule-Based Validation: Custom rule function execution, rule result parsing (error/warning extraction), exception handling and error collection - - Constraint-Based Validation: Entity constraint validation (required properties), relationship constraint validation (domain and range), constraint matching algorithms - -Quality Reporting: - - Issue Identification: Threshold-based issue detection (overall < 0.7, completeness < 0.8), issue type classification, severity assignment - - Recommendation Generation: Issue-based recommendation generation, score-based recommendation generation, actionable suggestion creation - - Report Serialization: JSON serialization (ISO timestamp formatting), YAML serialization (with PyYAML fallback), HTML report generation - -Automated Fixes: - - Duplicate Detection: Entity duplicate identification (using deduplication module), relationship duplicate identification - - Duplicate Merging: Property aggregation strategies, relationship reference updating, entity consolidation - - Conflict Resolution: Conflicting property value detection, resolution strategy selection, conflict merging - - Missing Property Completion: Schema-based required property identification, default value assignment, value inference - -Key Features: - - Multiple QA operation methods - - Quality assessment with method dispatch - - Method dispatchers with registry support - - Custom method registration capability - - Consistent interface across all methods - -Main Functions: - - assess_quality: Quality assessment wrapper - - generate_quality_report: Quality report generation wrapper - - identify_quality_issues: Quality issue identification wrapper - - check_consistency: Consistency checking wrapper - - validate_completeness: Completeness validation wrapper - - calculate_quality_metrics: Quality metrics calculation wrapper - - validate_graph: Graph validation wrapper - - export_report: Report export wrapper - - fix_issues: Automated fixing wrapper - - get_qa_method: Get QA method by name - - list_available_methods: List registered methods - -Example Usage: - >>> from semantica.kg_qa.methods import assess_quality, generate_quality_report - >>> score = assess_quality(knowledge_graph, method="default") - >>> report = generate_quality_report(knowledge_graph, schema, method="default") -""" - -from typing import Any, Callable, Dict, List, Optional, Union - -from ..utils.exceptions import ConfigurationError, ProcessingError -from ..utils.logging import get_logger -from .automated_fixes import AutomatedFixer, FixResult -from .config import kg_qa_config -from .kg_quality_assessor import ( - CompletenessValidator, - ConsistencyChecker, - KGQualityAssessor, -) -from .quality_metrics import CompletenessMetrics, ConsistencyMetrics, QualityMetrics -from .registry import method_registry -from .reporting import QualityReport, QualityReporter -from .validation_engine import ValidationEngine - -logger = get_logger("kg_qa_methods") - - -def assess_quality(knowledge_graph: Any, method: str = "default", **kwargs) -> float: - """ - Assess overall quality of knowledge graph (convenience function). - - This is a user-friendly wrapper that assesses knowledge graph quality - using the specified method. - - Args: - knowledge_graph: Knowledge graph instance (object with entities - and relationships, or dict with "entities" and - "relationships" keys) - method: Assessment method (default: "default") - - "default": Use KGQualityAssessor with default settings - - "comprehensive": Comprehensive assessment with all metrics - - "quick": Quick assessment with basic metrics - **kwargs: Additional options passed to KGQualityAssessor - - Returns: - float: Overall quality score between 0.0 and 1.0 (higher is better) - - Examples: - >>> from semantica.kg_qa.methods import assess_quality - >>> score = assess_quality(knowledge_graph, method="default") - >>> quick_score = assess_quality(knowledge_graph, method="quick") - """ - custom_method = method_registry.get("assess", method) - if custom_method: - try: - return custom_method(knowledge_graph, **kwargs) - except Exception as e: - logger.warning( - f"Custom method {method} failed: {e}, falling back to default" - ) - - try: - config = kg_qa_config.get_method_config("assess") - config.update(kwargs) - - assessor = KGQualityAssessor(**config) - return assessor.assess_overall_quality(knowledge_graph) - - except Exception as e: - logger.error(f"Failed to assess quality: {e}") - raise - - -def generate_quality_report( - knowledge_graph: Any, - schema: Optional[Dict[str, Any]] = None, - method: str = "default", - **kwargs, -) -> QualityReport: - """ - Generate comprehensive quality report (convenience function). - - This is a user-friendly wrapper that generates a quality report using - the specified method. - - Args: - knowledge_graph: Knowledge graph instance - schema: Optional schema definition for validation - method: Report generation method (default: "default") - - "default": Use KGQualityAssessor with default settings - - "detailed": Detailed report with all issues - - "summary": Summary report only - **kwargs: Additional options passed to KGQualityAssessor - - Returns: - QualityReport: Comprehensive quality report containing: - - timestamp: Report generation timestamp - - overall_score: Overall quality score - - completeness_score: Completeness score - - consistency_score: Consistency score - - issues: List of identified quality issues - - recommendations: List of improvement recommendations - - metadata: Additional report metadata - - Examples: - >>> from semantica.kg_qa.methods import generate_quality_report - >>> report = generate_quality_report(knowledge_graph, schema, method="default") - """ - custom_method = method_registry.get("report", method) - if custom_method: - try: - return custom_method(knowledge_graph, schema, **kwargs) - except Exception as e: - logger.warning( - f"Custom method {method} failed: {e}, falling back to default" - ) - - try: - config = kg_qa_config.get_method_config("report") - config.update(kwargs) - - assessor = KGQualityAssessor(**config) - return assessor.generate_quality_report(knowledge_graph, schema) - - except Exception as e: - logger.error(f"Failed to generate quality report: {e}") - raise - - -def identify_quality_issues( - knowledge_graph: Any, - schema: Optional[Dict[str, Any]] = None, - method: str = "default", - **kwargs, -) -> List[Dict[str, Any]]: - """ - Identify quality issues in knowledge graph (convenience function). - - This is a user-friendly wrapper that identifies quality issues using - the specified method. - - Args: - knowledge_graph: Knowledge graph instance - schema: Optional schema for validation - method: Issue identification method (default: "default") - **kwargs: Additional options passed to KGQualityAssessor - - Returns: - list: List of quality issue dictionaries, each containing: - - id: Issue identifier - - type: Issue type (e.g., "completeness", "consistency") - - severity: Issue severity ("low", "medium", "high") - - description: Issue description - - entity_id: Related entity ID (if applicable) - - relationship_id: Related relationship ID (if applicable) - - Examples: - >>> from semantica.kg_qa.methods import identify_quality_issues - >>> issues = identify_quality_issues(knowledge_graph, schema, method="default") - """ - custom_method = method_registry.get("assess", method) - if custom_method: - try: - return custom_method(knowledge_graph, schema, **kwargs) - except Exception as e: - logger.warning( - f"Custom method {method} failed: {e}, falling back to default" - ) - - try: - config = kg_qa_config.get_method_config("assess") - config.update(kwargs) - - assessor = KGQualityAssessor(**config) - return assessor.identify_quality_issues(knowledge_graph, schema) - - except Exception as e: - logger.error(f"Failed to identify quality issues: {e}") - raise - - -def check_consistency( - knowledge_graph: Any, - consistency_type: str = "logical", - method: str = "default", - **kwargs, -) -> Union[bool, Dict[str, bool]]: - """ - Check consistency of knowledge graph (convenience function). - - This is a user-friendly wrapper that checks consistency using - the specified method. - - Args: - knowledge_graph: Knowledge graph instance - consistency_type: Type of consistency to check (default: "logical") - - "logical": Logical consistency checking - - "temporal": Temporal consistency checking - - "hierarchical": Hierarchical consistency checking - - "all": All consistency checks (returns dict) - method: Consistency checking method (default: "default") - **kwargs: Additional options passed to ConsistencyChecker - - Returns: - bool or dict: Consistency check result(s) - - If consistency_type is "all", returns dict with keys: - "logical", "temporal", "hierarchical" - - Otherwise returns bool (True if consistent) - - Examples: - >>> from semantica.kg_qa.methods import check_consistency - >>> is_consistent = check_consistency(knowledge_graph, consistency_type="logical") - >>> all_checks = check_consistency(knowledge_graph, consistency_type="all") - """ - custom_method = method_registry.get("consistency", method) - if custom_method: - try: - return custom_method(knowledge_graph, consistency_type, **kwargs) - except Exception as e: - logger.warning( - f"Custom method {method} failed: {e}, falling back to default" - ) - - try: - config = kg_qa_config.get_method_config("consistency") - config.update(kwargs) - - checker = ConsistencyChecker(**config) - - if consistency_type == "all": - return { - "logical": checker.check_logical_consistency(knowledge_graph), - "temporal": checker.check_temporal_consistency(knowledge_graph), - "hierarchical": checker.check_hierarchical_consistency(knowledge_graph), - } - elif consistency_type == "logical": - return checker.check_logical_consistency(knowledge_graph) - elif consistency_type == "temporal": - return checker.check_temporal_consistency(knowledge_graph) - elif consistency_type == "hierarchical": - return checker.check_hierarchical_consistency(knowledge_graph) - else: - raise ValueError(f"Unknown consistency type: {consistency_type}") - - except Exception as e: - logger.error(f"Failed to check consistency: {e}") - raise - - -def validate_completeness( - entities: Optional[List[Dict[str, Any]]] = None, - relationships: Optional[List[Dict[str, Any]]] = None, - properties: Optional[Dict[str, Any]] = None, - schema: Dict[str, Any] = None, - completeness_type: str = "entity", - method: str = "default", - **kwargs, -) -> Union[bool, Dict[str, bool]]: - """ - Validate completeness of knowledge graph (convenience function). - - This is a user-friendly wrapper that validates completeness using - the specified method. - - Args: - entities: Optional list of entity dictionaries - relationships: Optional list of relationship dictionaries - properties: Optional properties dictionary - schema: Schema definition containing constraints - completeness_type: Type of completeness to validate (default: "entity") - - "entity": Entity completeness validation - - "relationship": Relationship completeness validation - - "property": Property completeness validation - - "all": All completeness checks (returns dict) - method: Completeness validation method (default: "default") - **kwargs: Additional options passed to CompletenessValidator - - Returns: - bool or dict: Completeness validation result(s) - - If completeness_type is "all", returns dict with keys: - "entity", "relationship", "property" - - Otherwise returns bool (True if complete) - - Examples: - >>> from semantica.kg_qa.methods import validate_completeness - >>> is_complete = validate_completeness(entities, schema, completeness_type="entity") - >>> all_checks = validate_completeness(entities, relationships, properties, schema, completeness_type="all") - """ - custom_method = method_registry.get("completeness", method) - if custom_method: - try: - return custom_method( - entities, relationships, properties, schema, completeness_type, **kwargs - ) - except Exception as e: - logger.warning( - f"Custom method {method} failed: {e}, falling back to default" - ) - - try: - config = kg_qa_config.get_method_config("completeness") - config.update(kwargs) - - validator = CompletenessValidator(**config) - - if completeness_type == "all": - results = {} - if entities and schema: - results["entity"] = validator.validate_entity_completeness( - entities, schema - ) - if relationships and schema: - results["relationship"] = validator.validate_relationship_completeness( - relationships, schema - ) - if properties and schema: - results["property"] = validator.validate_property_completeness( - properties, schema - ) - return results - elif completeness_type == "entity": - if not entities or not schema: - raise ValueError( - "entities and schema are required for entity completeness validation" - ) - return validator.validate_entity_completeness(entities, schema) - elif completeness_type == "relationship": - if not relationships or not schema: - raise ValueError( - "relationships and schema are required for relationship completeness validation" - ) - return validator.validate_relationship_completeness(relationships, schema) - elif completeness_type == "property": - if not properties or not schema: - raise ValueError( - "properties and schema are required for property completeness validation" - ) - return validator.validate_property_completeness(properties, schema) - else: - raise ValueError(f"Unknown completeness type: {completeness_type}") - - except Exception as e: - logger.error(f"Failed to validate completeness: {e}") - raise - - -def calculate_quality_metrics( - knowledge_graph: Any, - metrics_type: str = "overall", - method: str = "default", - **kwargs, -) -> Union[float, Dict[str, float]]: - """ - Calculate quality metrics for knowledge graph (convenience function). - - This is a user-friendly wrapper that calculates quality metrics using - the specified method. - - Args: - knowledge_graph: Knowledge graph instance - metrics_type: Type of metrics to calculate (default: "overall") - - "overall": Overall quality score - - "entity": Entity quality score - - "relationship": Relationship quality score - - "completeness": Completeness metrics - - "consistency": Consistency metrics - - "all": All metrics (returns dict) - method: Metrics calculation method (default: "default") - **kwargs: Additional options passed to QualityMetrics - - Returns: - float or dict: Quality metric(s) - - If metrics_type is "all", returns dict with all metrics - - Otherwise returns float score - - Examples: - >>> from semantica.kg_qa.methods import calculate_quality_metrics - >>> score = calculate_quality_metrics(knowledge_graph, metrics_type="overall") - >>> all_metrics = calculate_quality_metrics(knowledge_graph, metrics_type="all") - """ - custom_method = method_registry.get("metrics", method) - if custom_method: - try: - return custom_method(knowledge_graph, metrics_type, **kwargs) - except Exception as e: - logger.warning( - f"Custom method {method} failed: {e}, falling back to default" - ) - - try: - config = kg_qa_config.get_method_config("metrics") - config.update(kwargs) - - quality_metrics = QualityMetrics(**config) - entities = getattr( - knowledge_graph, - "entities", - knowledge_graph.get("entities", []) - if isinstance(knowledge_graph, dict) - else [], - ) - relationships = getattr( - knowledge_graph, - "relationships", - knowledge_graph.get("relationships", []) - if isinstance(knowledge_graph, dict) - else [], - ) - - if metrics_type == "all": - return { - "overall": quality_metrics.calculate_overall_score(knowledge_graph), - "entity": quality_metrics.calculate_entity_quality(entities) - if entities - else 0.0, - "relationship": quality_metrics.calculate_relationship_quality( - relationships - ) - if relationships - else 0.0, - } - elif metrics_type == "overall": - return quality_metrics.calculate_overall_score(knowledge_graph) - elif metrics_type == "entity": - if not entities: - raise ValueError( - "Knowledge graph must have entities for entity quality calculation" - ) - return quality_metrics.calculate_entity_quality(entities) - elif metrics_type == "relationship": - if not relationships: - raise ValueError( - "Knowledge graph must have relationships for relationship quality calculation" - ) - return quality_metrics.calculate_relationship_quality(relationships) - else: - raise ValueError(f"Unknown metrics type: {metrics_type}") - - except Exception as e: - logger.error(f"Failed to calculate quality metrics: {e}") - raise - - -def validate_graph( - knowledge_graph: Any, - rules: Optional[List[Callable]] = None, - method: str = "default", - **kwargs, -) -> Any: - """ - Validate knowledge graph (convenience function). - - This is a user-friendly wrapper that validates a knowledge graph using - the specified method. - - Args: - knowledge_graph: Knowledge graph instance to validate - rules: Optional list of validation rule functions - method: Validation method (default: "default") - - "default": Default validation with stored rules - - "custom": Custom rule validation - - "constraints": Constraint-based validation - **kwargs: Additional options passed to ValidationEngine - - Returns: - ValidationResult: Validation result containing: - - valid: True if no errors, False otherwise - - errors: List of error messages - - warnings: List of warning messages - - metadata: Additional validation metadata - - Examples: - >>> from semantica.kg_qa.methods import validate_graph - >>> result = validate_graph(knowledge_graph, method="default") - """ - custom_method = method_registry.get("validate", method) - if custom_method: - try: - return custom_method(knowledge_graph, rules, **kwargs) - except Exception as e: - logger.warning( - f"Custom method {method} failed: {e}, falling back to default" - ) - - try: - config = kg_qa_config.get_method_config("validate") - config.update(kwargs) - - engine = ValidationEngine(**config) - return engine.validate(knowledge_graph, rules) - - except Exception as e: - logger.error(f"Failed to validate graph: {e}") - raise - - -def export_report( - report: QualityReport, format: str = "json", method: str = "default", **kwargs -) -> str: - """ - Export quality report to specified format (convenience function). - - This is a user-friendly wrapper that exports a quality report using - the specified method. - - Args: - report: Quality report to export - format: Export format (default: "json") - - "json": JSON format - - "yaml": YAML format - - "html": HTML format (planned) - method: Export method (default: "default") - **kwargs: Additional options passed to QualityReporter - - Returns: - str: Exported report as string in the specified format - - Examples: - >>> from semantica.kg_qa.methods import export_report - >>> json_report = export_report(report, format="json") - >>> yaml_report = export_report(report, format="yaml") - """ - custom_method = method_registry.get("report", method) - if custom_method: - try: - return custom_method(report, format, **kwargs) - except Exception as e: - logger.warning( - f"Custom method {method} failed: {e}, falling back to default" - ) - - try: - config = kg_qa_config.get_method_config("report") - config.update(kwargs) - - reporter = QualityReporter(**config) - return reporter.export_report(report, format=format) - - except Exception as e: - logger.error(f"Failed to export report: {e}") - raise - - -def fix_issues( - knowledge_graph: Any, - fix_type: str = "duplicates", - schema: Optional[Dict[str, Any]] = None, - method: str = "default", - **kwargs, -) -> FixResult: - """ - Fix quality issues in knowledge graph (convenience function). - - This is a user-friendly wrapper that fixes quality issues using - the specified method. - - Args: - knowledge_graph: Knowledge graph instance - fix_type: Type of fix to apply (default: "duplicates") - - "duplicates": Fix duplicate entities - - "inconsistencies": Fix inconsistencies - - "missing_properties": Fix missing properties - - "all": Apply all fixes - schema: Optional schema definition (required for missing_properties) - method: Fixing method (default: "default") - **kwargs: Additional options passed to AutomatedFixer - - Returns: - FixResult: Fix result containing: - - success: Whether fixing was successful - - fixed_count: Number of issues fixed - - errors: List of error messages - - metadata: Additional fix metadata - - Examples: - >>> from semantica.kg_qa.methods import fix_issues - >>> result = fix_issues(knowledge_graph, fix_type="duplicates") - >>> result = fix_issues(knowledge_graph, fix_type="missing_properties", schema=schema) - """ - custom_method = method_registry.get("fix", method) - if custom_method: - try: - return custom_method(knowledge_graph, fix_type, schema, **kwargs) - except Exception as e: - logger.warning( - f"Custom method {method} failed: {e}, falling back to default" - ) - - try: - config = kg_qa_config.get_method_config("fix") - config.update(kwargs) - - fixer = AutomatedFixer(**config) - - if fix_type == "all": - # Apply all fixes sequentially - results = [] - results.append(fixer.fix_duplicates(knowledge_graph)) - results.append(fixer.fix_inconsistencies(knowledge_graph)) - if schema: - results.append(fixer.fix_missing_properties(knowledge_graph, schema)) - - total_fixed = sum(r.fixed_count for r in results) - all_errors = [] - for r in results: - all_errors.extend(r.errors) - - return FixResult( - success=all(r.success for r in results), - fixed_count=total_fixed, - errors=all_errors, - metadata={"fixes_applied": [fix_type for r in results if r.success]}, - ) - elif fix_type == "duplicates": - return fixer.fix_duplicates(knowledge_graph) - elif fix_type == "inconsistencies": - return fixer.fix_inconsistencies(knowledge_graph) - elif fix_type == "missing_properties": - if not schema: - raise ValueError("schema is required for missing_properties fix") - return fixer.fix_missing_properties(knowledge_graph, schema) - else: - raise ValueError(f"Unknown fix type: {fix_type}") - - except Exception as e: - logger.error(f"Failed to fix issues: {e}") - raise - - -def get_qa_method(task: str, name: str) -> Optional[Callable]: - """Get QA method by task and name.""" - return method_registry.get(task, name) - - -def list_available_methods(task: Optional[str] = None) -> Dict[str, List[str]]: - """List all registered QA methods.""" - return method_registry.list_all(task) - - -# Register default methods -method_registry.register("assess", "default", assess_quality) -method_registry.register("report", "default", generate_quality_report) -method_registry.register("consistency", "default", check_consistency) -method_registry.register("completeness", "default", validate_completeness) -method_registry.register("metrics", "default", calculate_quality_metrics) -method_registry.register("validate", "default", validate_graph) -method_registry.register("fix", "default", fix_issues) diff --git a/semantica/kg_qa/quality_metrics.py b/semantica/kg_qa/quality_metrics.py deleted file mode 100644 index 2d25239a..00000000 --- a/semantica/kg_qa/quality_metrics.py +++ /dev/null @@ -1,471 +0,0 @@ -""" -Quality Metrics Module - -This module provides comprehensive quality metrics calculation for the Semantica -framework, enabling quantitative assessment of knowledge graph quality across -multiple dimensions. - -Key Features: - - Overall quality score calculation - - Entity quality metrics - - Relationship quality metrics - - Completeness metrics (entity, relationship, property) - - Consistency metrics (logical, temporal, hierarchical) - -Main Classes: - - QualityMetrics: Overall quality metrics calculator - - CompletenessMetrics: Completeness metrics calculator - - ConsistencyMetrics: Consistency metrics calculator - -Example Usage: - >>> from semantica.kg_qa import QualityMetrics - >>> metrics = QualityMetrics() - >>> score = metrics.calculate_overall_score(knowledge_graph) - >>> entity_score = metrics.calculate_entity_quality(entities) - -Author: Semantica Contributors -License: MIT -""" - -from dataclasses import dataclass -from typing import Any, Dict, List, Optional - -from ..utils.logging import get_logger -from ..utils.progress_tracker import get_progress_tracker - - -@dataclass -class QualityScore: - """ - Quality score dataclass. - - This dataclass represents a comprehensive quality score for a knowledge graph, - containing scores for different quality dimensions and optional metadata. - - Attributes: - overall: Overall quality score (0.0 to 1.0) - completeness: Completeness score (0.0 to 1.0) - consistency: Consistency score (0.0 to 1.0) - accuracy: Accuracy score (0.0 to 1.0) - metadata: Additional metadata dictionary (optional) - """ - - overall: float - completeness: float - consistency: float - accuracy: float - metadata: Optional[Dict[str, Any]] = None - - -class QualityMetrics: - """ - Quality metrics calculator. - - This class provides overall quality metrics calculation for knowledge graphs, - aggregating entity quality, relationship quality, and consistency into - comprehensive quality scores. - - Features: - - Overall quality score calculation - - Entity quality assessment - - Relationship quality assessment - - Weighted aggregation of metrics - - Example Usage: - >>> metrics = QualityMetrics() - >>> score = metrics.calculate_overall_score(knowledge_graph) - >>> entity_score = metrics.calculate_entity_quality(entities) - """ - - def __init__(self, **kwargs): - """ - Initialize quality metrics calculator. - - Sets up the calculator with configuration options. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("quality_metrics") - self.config = kwargs - - # Initialize progress tracker - self.progress_tracker = get_progress_tracker() - - self.logger.debug("Quality metrics calculator initialized") - - def calculate_overall_score(self, knowledge_graph: Any) -> float: - """ - Calculate overall quality score. - - This method calculates an overall quality score by aggregating entity - quality and consistency metrics using weighted averaging (60% completeness, - 40% consistency). - - Args: - knowledge_graph: Knowledge graph instance (object with entities and - relationships, or dict with "entities" and "relationships") - - Returns: - float: Overall quality score between 0.0 and 1.0 (higher is better) - """ - # Track quality calculation - tracking_id = self.progress_tracker.start_tracking( - file=None, - module="kg_qa", - submodule="QualityMetrics", - message="Calculating quality metrics", - ) - - try: - self.progress_tracker.update_tracking( - tracking_id, message="Calculating entity quality..." - ) - completeness = self.calculate_entity_quality(knowledge_graph) - self.progress_tracker.update_tracking( - tracking_id, message="Calculating consistency..." - ) - consistency = self._calculate_consistency(knowledge_graph) - - self.progress_tracker.update_tracking( - tracking_id, message="Aggregating scores..." - ) - # Weighted average - overall = (0.6 * completeness) + (0.4 * consistency) - - result = min(1.0, max(0.0, overall)) - self.progress_tracker.stop_tracking( - tracking_id, - status="completed", - message=f"Overall quality score: {result:.2f}", - ) - return result - - except Exception as e: - self.progress_tracker.stop_tracking( - tracking_id, status="failed", message=str(e) - ) - raise - - def calculate_entity_quality(self, entities: List[Dict[str, Any]]) -> float: - """ - Calculate entity quality score. - - This method calculates a quality score for entities based on the presence - of required fields (ID and type). Each entity is scored, and the average - is returned. - - Args: - entities: List of entity dictionaries - - Returns: - float: Entity quality score between 0.0 and 1.0 (average across all entities) - """ - if not entities: - return 0.0 - - # Calculate quality based on entity completeness - scores = [] - for entity in entities: - # Check required fields - has_id = "id" in entity or "uri" in entity - has_type = "type" in entity - - score = 0.0 - if has_id: - score += 0.5 - if has_type: - score += 0.5 - - scores.append(score) - - return sum(scores) / len(scores) if scores else 0.0 - - def calculate_relationship_quality( - self, relationships: List[Dict[str, Any]] - ) -> float: - """ - Calculate relationship quality score. - - This method calculates a quality score for relationships based on the - presence of required fields (source/subject, target/object, type/predicate). - Each relationship is scored, and the average is returned. - - Args: - relationships: List of relationship dictionaries - - Returns: - float: Relationship quality score between 0.0 and 1.0 (average across all relationships) - """ - if not relationships: - return 0.0 - - scores = [] - for rel in relationships: - # Check required fields - has_source = "source" in rel or "subject" in rel - has_target = "target" in rel or "object" in rel - has_type = "type" in rel or "predicate" in rel - - score = 0.0 - if has_source: - score += 0.33 - if has_target: - score += 0.33 - if has_type: - score += 0.34 - - scores.append(score) - - return sum(scores) / len(scores) if scores else 0.0 - - def _calculate_consistency(self, knowledge_graph: Any) -> float: - """ - Calculate consistency score (simplified). - - This is a placeholder method. In practice, this would check for logical - inconsistencies in the knowledge graph. - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - float: Consistency score between 0.0 and 1.0 (placeholder: 0.8) - """ - # In practice, this would check for logical inconsistencies - return 0.8 # Placeholder - - -class CompletenessMetrics: - """ - Completeness metrics calculator. - - This class provides completeness metrics calculation for knowledge graphs, - assessing whether entities, relationships, and properties meet schema - requirements for completeness. - - Features: - - Entity completeness calculation - - Relationship completeness calculation - - Property completeness calculation - - Schema-based validation - - Example Usage: - >>> metrics = CompletenessMetrics() - >>> score = metrics.calculate_entity_completeness(entities, schema) - >>> rel_score = metrics.calculate_relationship_completeness(relationships, schema) - """ - - def __init__(self, **kwargs): - """ - Initialize completeness metrics calculator. - - Sets up the calculator with configuration options. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("completeness_metrics") - self.config = kwargs - - self.logger.debug("Completeness metrics calculator initialized") - - def calculate_entity_completeness( - self, entities: List[Dict[str, Any]], schema: Dict[str, Any] - ) -> float: - """ - Calculate entity completeness. - - This method calculates completeness scores for entities by checking - whether they have all required properties as defined in the schema. - Returns the average completeness score across all entities. - - Args: - entities: List of entity dictionaries - schema: Schema definition containing constraints with required_props - for each entity type - - Returns: - float: Completeness score between 0.0 and 1.0 (average across entities) - """ - if not entities: - return 0.0 - - constraints = schema.get("constraints", {}) - scores = [] - - for entity in entities: - entity_type = entity.get("type") - if not entity_type: - scores.append(0.0) - continue - - constraint = constraints.get(entity_type, {}) - required_props = constraint.get("required_props", []) - - if not required_props: - scores.append(1.0) - continue - - # Count how many required properties are present - present_props = sum(1 for prop in required_props if prop in entity) - completeness = ( - present_props / len(required_props) if required_props else 1.0 - ) - - scores.append(completeness) - - return sum(scores) / len(scores) if scores else 0.0 - - def calculate_property_completeness( - self, properties: Dict[str, Any], schema: Dict[str, Any] - ) -> float: - """ - Calculate property completeness. - - This method calculates completeness scores for properties by checking - whether entity types have all required properties as defined in the schema. - - Args: - properties: Properties dictionary (mapping entity types to property dictionaries) - schema: Schema definition containing constraints with required_props - - Returns: - float: Completeness score between 0.0 and 1.0 (average across entity types) - """ - constraints = schema.get("constraints", {}) - scores = [] - - for entity_type, constraint in constraints.items(): - required_props = constraint.get("required_props", []) - - if entity_type in properties: - entity_props = properties[entity_type] - present_props = sum( - 1 for prop in required_props if prop in entity_props - ) - completeness = ( - present_props / len(required_props) if required_props else 1.0 - ) - scores.append(completeness) - - return sum(scores) / len(scores) if scores else 1.0 - - def calculate_relationship_completeness( - self, relationships: List[Dict[str, Any]], schema: Dict[str, Any] - ) -> float: - """ - Calculate relationship completeness. - - This method calculates completeness scores for relationships by checking - whether they have all required fields (source/subject, target/object, - type/predicate). Returns the average completeness score. - - Args: - relationships: List of relationship dictionaries - schema: Schema definition (currently unused, reserved for future - relationship-specific constraints) - - Returns: - float: Completeness score between 0.0 and 1.0 (average across relationships) - """ - if not relationships: - return 0.0 - - # Check if relationships have required fields - scores = [] - for rel in relationships: - has_source = "source" in rel or "subject" in rel - has_target = "target" in rel or "object" in rel - has_type = "type" in rel or "predicate" in rel - - completeness = (has_source + has_target + has_type) / 3.0 - scores.append(completeness) - - return sum(scores) / len(scores) if scores else 0.0 - - -class ConsistencyMetrics: - """ - Consistency metrics calculator. - - This class provides consistency metrics calculation for knowledge graphs, - assessing logical, temporal, and hierarchical consistency. - - Features: - - Logical consistency calculation - - Temporal consistency calculation - - Hierarchical consistency calculation - - Example Usage: - >>> metrics = ConsistencyMetrics() - >>> logical_score = metrics.calculate_logical_consistency(knowledge_graph) - >>> temporal_score = metrics.calculate_temporal_consistency(knowledge_graph) - """ - - def __init__(self, **kwargs): - """ - Initialize consistency metrics calculator. - - Sets up the calculator with configuration options. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("consistency_metrics") - self.config = kwargs - - self.logger.debug("Consistency metrics calculator initialized") - - def calculate_logical_consistency(self, knowledge_graph: Any) -> float: - """ - Calculate logical consistency. - - This method calculates a logical consistency score by checking for - logical contradictions, conflicting relationships, and inconsistent - property values. Currently returns a placeholder value. - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - float: Logical consistency score between 0.0 and 1.0 (placeholder: 0.9) - """ - # In practice, this would use a reasoner - # For now, return a placeholder - return 0.9 - - def calculate_temporal_consistency(self, knowledge_graph: Any) -> float: - """ - Calculate temporal consistency. - - This method calculates a temporal consistency score by checking for - temporal contradictions, invalid time ranges, and conflicting - temporal relationships. Currently returns a placeholder value. - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - float: Temporal consistency score between 0.0 and 1.0 (placeholder: 0.85) - """ - # Check for temporal contradictions - return 0.85 - - def calculate_hierarchical_consistency(self, knowledge_graph: Any) -> float: - """ - Calculate hierarchical consistency. - - This method calculates a hierarchical consistency score by checking for - hierarchical contradictions such as circular inheritance, invalid - parent-child relationships, and conflicting hierarchical structures. - Currently returns a placeholder value. - - Args: - knowledge_graph: Knowledge graph instance - - Returns: - float: Hierarchical consistency score between 0.0 and 1.0 (placeholder: 0.9) - """ - # Check for hierarchical contradictions (e.g., circular inheritance) - return 0.9 diff --git a/semantica/kg_qa/registry.py b/semantica/kg_qa/registry.py deleted file mode 100644 index 938a2da2..00000000 --- a/semantica/kg_qa/registry.py +++ /dev/null @@ -1,131 +0,0 @@ -""" -Method Registry Module for KG QA - -This module provides a method registry system for registering custom KG QA methods, -enabling extensibility and community contributions to the quality assurance toolkit. - -Supported Registration Types: - - Method Registry: Register custom QA methods for: - * "assess": Quality assessment methods - * "report": Report generation methods - * "consistency": Consistency checking methods - * "completeness": Completeness validation methods - * "metrics": Quality metrics calculation methods - * "validate": Validation engine methods - * "fix": Automated fixing methods - -Algorithms Used: - - Registry Pattern: Dictionary-based registration and lookup - - Dynamic Registration: Runtime function registration - - Type Checking: Type validation for registered components - - Lookup Algorithms: Hash-based O(1) lookup for methods - - Task-based Organization: Hierarchical organization by task type - -Key Features: - - Method registry for custom QA methods - - Task-based method organization (assess, report, consistency, completeness, metrics, validate, fix) - - Dynamic registration and unregistration - - Easy discovery of available methods - - Support for community-contributed extensions - -Main Classes: - - MethodRegistry: Registry for custom KG QA methods - -Global Instances: - - method_registry: Global method registry instance - -Example Usage: - >>> from semantica.kg_qa.registry import method_registry - >>> method_registry.register("assess", "custom_method", custom_assessment_function) - >>> available = method_registry.list_all("assess") -""" - -from typing import Any, Callable, Dict, List, Optional - - -class MethodRegistry: - """Registry for custom KG QA methods.""" - - _methods: Dict[str, Dict[str, Callable]] = { - "assess": {}, - "report": {}, - "consistency": {}, - "completeness": {}, - "metrics": {}, - "validate": {}, - "fix": {}, - } - - @classmethod - def register(cls, task: str, name: str, method_func: Callable): - """ - Register a custom QA method. - - Args: - task: Task type ("assess", "report", "consistency", "completeness", "metrics", "validate", "fix") - name: Method name - method_func: Method function - """ - if task not in cls._methods: - cls._methods[task] = {} - cls._methods[task][name] = method_func - - @classmethod - def get(cls, task: str, name: str) -> Optional[Callable]: - """ - Get method by task and name. - - Args: - task: Task type ("assess", "report", "consistency", "completeness", "metrics", "validate", "fix") - name: Method name - - Returns: - Method function or None - """ - return cls._methods.get(task, {}).get(name) - - @classmethod - def list_all(cls, task: Optional[str] = None) -> Dict[str, List[str]]: - """ - List all registered methods. - - Args: - task: Optional task type to filter by - - Returns: - Dictionary mapping task types to method names - """ - if task: - return {task: list(cls._methods.get(task, {}).keys())} - return {t: list(m.keys()) for t, m in cls._methods.items()} - - @classmethod - def unregister(cls, task: str, name: str): - """ - Unregister a method. - - Args: - task: Task type ("assess", "report", "consistency", "completeness", "metrics", "validate", "fix") - name: Method name - """ - if task in cls._methods and name in cls._methods[task]: - del cls._methods[task][name] - - @classmethod - def clear(cls, task: Optional[str] = None): - """ - Clear all registered methods for a task or all tasks. - - Args: - task: Optional task type to clear (clears all if None) - """ - if task: - if task in cls._methods: - cls._methods[task].clear() - else: - for task_dict in cls._methods.values(): - task_dict.clear() - - -# Global registry -method_registry = MethodRegistry() diff --git a/semantica/kg_qa/reporting.py b/semantica/kg_qa/reporting.py deleted file mode 100644 index 719fee81..00000000 --- a/semantica/kg_qa/reporting.py +++ /dev/null @@ -1,491 +0,0 @@ -""" -Quality Reporting Module - -This module provides comprehensive quality reporting capabilities for the -Semantica framework, enabling generation of quality reports, issue tracking, -and improvement suggestions. - -Key Features: - - Quality report generation - - Issue identification and tracking - - Improvement suggestions generation - - Report export (JSON, YAML, HTML) - - Issue management (add, get, list, resolve) - -Main Classes: - - QualityReporter: Quality report generation engine - - IssueTracker: Issue tracking and management - - ImprovementSuggestions: Improvement suggestions generator - -Example Usage: - >>> from semantica.kg_qa import QualityReporter - >>> reporter = QualityReporter() - >>> report = reporter.generate_report(knowledge_graph, quality_metrics) - >>> json_report = reporter.export_report(report, format="json") - -Author: Semantica Contributors -License: MIT -""" - -from dataclasses import dataclass, field -from datetime import datetime -from typing import Any, Dict, List, Optional - -from ..utils.logging import get_logger -from ..utils.progress_tracker import get_progress_tracker - - -@dataclass -class QualityIssue: - """ - Quality issue dataclass. - - This dataclass represents a quality issue found in a knowledge graph, - containing issue identification, type, severity, and related entity/relationship - information. - - Attributes: - id: Unique issue identifier - type: Issue type (e.g., "completeness", "consistency", "quality") - severity: Issue severity ("low", "medium", "high") - description: Human-readable issue description - entity_id: Related entity ID (optional) - relationship_id: Related relationship ID (optional) - metadata: Additional issue metadata dictionary - """ - - id: str - type: str - severity: str - description: str - entity_id: Optional[str] = None - relationship_id: Optional[str] = None - metadata: Dict[str, Any] = field(default_factory=dict) - - -@dataclass -class QualityReport: - """ - Quality report dataclass. - - This dataclass represents a comprehensive quality report for a knowledge graph, - containing quality scores, identified issues, recommendations, and metadata. - - Attributes: - timestamp: Report generation timestamp - overall_score: Overall quality score (0.0 to 1.0) - completeness_score: Completeness score (0.0 to 1.0) - consistency_score: Consistency score (0.0 to 1.0) - issues: List of identified quality issues - recommendations: List of improvement recommendations - metadata: Additional report metadata dictionary - """ - - timestamp: datetime - overall_score: float - completeness_score: float - consistency_score: float - issues: List[QualityIssue] = field(default_factory=list) - recommendations: List[str] = field(default_factory=list) - metadata: Dict[str, Any] = field(default_factory=dict) - - -class QualityReporter: - """ - Quality report generation engine. - - This class provides quality report generation capabilities, including issue - identification, recommendation generation, and report export in various formats. - - Features: - - Quality report generation - - Issue identification - - Recommendation generation - - Report export (JSON, YAML, HTML) - - Example Usage: - >>> reporter = QualityReporter() - >>> report = reporter.generate_report(knowledge_graph, quality_metrics) - >>> json_report = reporter.export_report(report, format="json") - """ - - def __init__(self, **kwargs): - """ - Initialize quality reporter. - - Sets up the reporter with configuration options. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("quality_reporter") - self.config = kwargs - - # Initialize progress tracker - self.progress_tracker = get_progress_tracker() - - self.logger.debug("Quality reporter initialized") - - def generate_report( - self, knowledge_graph: Any, quality_metrics: Dict[str, float] - ) -> QualityReport: - """ - Generate quality report. - - This method generates a comprehensive quality report by identifying - issues based on quality metrics and generating recommendations. - - Args: - knowledge_graph: Knowledge graph instance - quality_metrics: Quality metrics dictionary containing: - - overall: Overall quality score - - completeness: Completeness score - - consistency: Consistency score - - Returns: - QualityReport: Comprehensive quality report with scores, issues, - and recommendations - """ - # Track report generation - tracking_id = self.progress_tracker.start_tracking( - file=None, - module="kg_qa", - submodule="QualityReporter", - message="Generating quality report", - ) - - try: - self.progress_tracker.update_tracking( - tracking_id, message="Identifying issues..." - ) - issues = self._identify_issues(knowledge_graph, quality_metrics) - self.progress_tracker.update_tracking( - tracking_id, message="Generating recommendations..." - ) - recommendations = self._generate_recommendations(issues) - - report = QualityReport( - timestamp=datetime.now(), - overall_score=quality_metrics.get("overall", 0.0), - completeness_score=quality_metrics.get("completeness", 0.0), - consistency_score=quality_metrics.get("consistency", 0.0), - issues=issues, - recommendations=recommendations, - ) - - self.progress_tracker.stop_tracking( - tracking_id, - status="completed", - message=f"Generated quality report with {len(issues)} issues", - ) - return report - - except Exception as e: - self.progress_tracker.stop_tracking( - tracking_id, status="failed", message=str(e) - ) - raise - - def export_report(self, report: QualityReport, format: str = "json") -> str: - """ - Export report to specified format. - - This method exports a quality report to the specified format (JSON, YAML, - or HTML). For unsupported formats, returns string representation. - - Args: - report: Quality report to export - format: Export format ("json", "yaml", or "html", default: "json") - - Returns: - str: Exported report as string in the specified format - - Note: - YAML export requires the `pyyaml` library. If not available, falls - back to string representation. - """ - if format == "json": - import json - - return json.dumps( - { - "timestamp": report.timestamp.isoformat(), - "overall_score": report.overall_score, - "completeness_score": report.completeness_score, - "consistency_score": report.consistency_score, - "issues": [ - { - "id": issue.id, - "type": issue.type, - "severity": issue.severity, - "description": issue.description, - } - for issue in report.issues - ], - "recommendations": report.recommendations, - }, - indent=2, - ) - - elif format == "yaml": - try: - import yaml - - return yaml.dump( - { - "timestamp": report.timestamp.isoformat(), - "overall_score": report.overall_score, - "issues": [ - { - "id": issue.id, - "type": issue.type, - "description": issue.description, - } - for issue in report.issues - ], - } - ) - except ImportError: - self.logger.warning( - "PyYAML not available, falling back to string representation" - ) - return str(report) - - else: - return str(report) - - def _identify_issues( - self, knowledge_graph: Any, metrics: Dict[str, float] - ) -> List[QualityIssue]: - """ - Identify quality issues. - - This method identifies quality issues based on quality metrics, - checking for low scores and generating appropriate issue objects. - - Args: - knowledge_graph: Knowledge graph instance - metrics: Quality metrics dictionary - - Returns: - list: List of identified quality issues - """ - issues = [] - - # Check for low scores - if metrics.get("overall", 1.0) < 0.7: - issues.append( - QualityIssue( - id="low_overall_score", - type="quality", - severity="high", - description="Overall quality score is below threshold", - ) - ) - - if metrics.get("completeness", 1.0) < 0.8: - issues.append( - QualityIssue( - id="low_completeness", - type="completeness", - severity="medium", - description="Completeness score is below threshold", - ) - ) - - return issues - - def _generate_recommendations(self, issues: List[QualityIssue]) -> List[str]: - """ - Generate improvement recommendations. - - This method generates improvement recommendations based on identified - quality issues, providing actionable suggestions for improving - knowledge graph quality. - - Args: - issues: List of quality issues - - Returns: - list: List of improvement recommendation strings - """ - recommendations = [] - - for issue in issues: - if issue.type == "completeness": - recommendations.append("Add missing required properties to entities") - elif issue.type == "consistency": - recommendations.append( - "Resolve consistency violations in the knowledge graph" - ) - - return recommendations - - -class IssueTracker: - """ - Issue tracking and management engine. - - This class provides issue tracking capabilities, enabling storage, retrieval, - filtering, and resolution of quality issues. - - Features: - - Issue storage and retrieval - - Issue filtering by severity - - Issue resolution tracking - - Example Usage: - >>> tracker = IssueTracker() - >>> tracker.add_issue(issue) - >>> issues = tracker.list_issues(severity="high") - >>> tracker.resolve_issue(issue_id) - """ - - def __init__(self, **kwargs): - """ - Initialize issue tracker. - - Sets up the tracker with configuration and initializes issue storage. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("issue_tracker") - self.config = kwargs - self.issues: Dict[str, QualityIssue] = {} - - # Initialize progress tracker - self.progress_tracker = get_progress_tracker() - - self.logger.debug("Issue tracker initialized") - - def add_issue(self, issue: QualityIssue) -> None: - """ - Add an issue to the tracker. - - This method adds a quality issue to the tracker's issue dictionary, - using the issue ID as the key. - - Args: - issue: Quality issue to add - """ - self.issues[issue.id] = issue - - def get_issue(self, issue_id: str) -> Optional[QualityIssue]: - """ - Get issue by ID. - - This method retrieves a quality issue from the tracker by its ID. - - Args: - issue_id: Issue identifier - - Returns: - QualityIssue: The issue if found, None otherwise - """ - return self.issues.get(issue_id) - - def list_issues(self, severity: Optional[str] = None) -> List[QualityIssue]: - """ - List issues, optionally filtered by severity. - - This method returns all tracked issues, optionally filtered by severity - level ("low", "medium", "high"). - - Args: - severity: Optional severity filter ("low", "medium", "high") - - Returns: - list: List of quality issues (filtered by severity if provided) - """ - issues = list(self.issues.values()) - - if severity: - issues = [i for i in issues if i.severity == severity] - - return issues - - def resolve_issue(self, issue_id: str) -> bool: - """ - Mark issue as resolved. - - This method removes an issue from the tracker, effectively marking - it as resolved. - - Args: - issue_id: Issue identifier to resolve - - Returns: - bool: True if issue was found and resolved, False otherwise - """ - if issue_id in self.issues: - del self.issues[issue_id] - return True - return False - - -class ImprovementSuggestions: - """ - Improvement suggestions generator. - - This class provides improvement suggestions generation capabilities, - analyzing quality reports and generating actionable recommendations - for improving knowledge graph quality. - - Features: - - Issue-based suggestions - - Score-based suggestions - - Actionable recommendations - - Example Usage: - >>> generator = ImprovementSuggestions() - >>> suggestions = generator.generate_suggestions(quality_report) - """ - - def __init__(self, **kwargs): - """ - Initialize improvement suggestions generator. - - Sets up the generator with configuration options. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("improvement_suggestions") - self.config = kwargs - - # Initialize progress tracker - self.progress_tracker = get_progress_tracker() - - self.logger.debug("Improvement suggestions generator initialized") - - def generate_suggestions(self, quality_report: QualityReport) -> List[str]: - """ - Generate improvement suggestions. - - This method generates improvement suggestions based on the quality report, - analyzing issues and scores to provide actionable recommendations. - - Args: - quality_report: Quality report containing scores and issues - - Returns: - list: List of improvement suggestion strings - """ - suggestions = [] - - # Based on issues - for issue in quality_report.issues: - if issue.type == "completeness": - suggestions.append(f"Improve completeness for {issue.description}") - elif issue.type == "consistency": - suggestions.append(f"Resolve consistency issue: {issue.description}") - - # Based on scores - if quality_report.overall_score < 0.7: - suggestions.append("Overall quality needs improvement") - - if quality_report.completeness_score < 0.8: - suggestions.append("Add missing required properties") - - return suggestions diff --git a/semantica/kg_qa/validation_engine.py b/semantica/kg_qa/validation_engine.py deleted file mode 100644 index f2b02b96..00000000 --- a/semantica/kg_qa/validation_engine.py +++ /dev/null @@ -1,344 +0,0 @@ -""" -Validation Engine Module - -This module provides comprehensive validation capabilities for the Semantica -framework, enabling rule-based and constraint-based validation of knowledge graphs. - -Key Features: - - Rule-based validation - - Constraint-based validation - - Custom validation rules - - Validation result reporting - -Main Classes: - - ValidationEngine: Main validation engine - - RuleValidator: Rule-based validation - - ConstraintValidator: Constraint-based validation - -Example Usage: - >>> from semantica.kg_qa import ValidationEngine - >>> engine = ValidationEngine() - >>> result = engine.validate(knowledge_graph, rules=[rule1, rule2]) - >>> engine.add_rule(custom_rule) - -Author: Semantica Contributors -License: MIT -""" - -from dataclasses import dataclass, field -from typing import Any, Callable, Dict, List, Optional - -from ..utils.exceptions import ValidationError -from ..utils.logging import get_logger -from ..utils.progress_tracker import get_progress_tracker - - -@dataclass -class ValidationResult: - """ - Validation result dataclass. - - This dataclass represents the result of a validation operation, containing - validation status, errors, warnings, and optional metadata. - - Attributes: - valid: Whether the validation passed (True if no errors) - errors: List of error messages (critical validation failures) - warnings: List of warning messages (non-critical issues) - metadata: Additional validation metadata dictionary - """ - - valid: bool - errors: List[str] = field(default_factory=list) - warnings: List[str] = field(default_factory=list) - metadata: Dict[str, Any] = field(default_factory=dict) - - -class ValidationEngine: - """ - Validation engine. - - This class provides rule-based validation capabilities for knowledge graphs, - enabling custom validation rules and constraint checking. - - Features: - - Custom validation rules - - Rule management (add, remove) - - Validation result reporting - - Error and warning collection - - Example Usage: - >>> engine = ValidationEngine() - >>> engine.add_rule(custom_validation_rule) - >>> result = engine.validate(knowledge_graph) - >>> if not result.valid: - ... print(f"Errors: {result.errors}") - """ - - def __init__(self, **kwargs): - """ - Initialize validation engine. - - Sets up the engine with configuration and initializes rule storage. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("validation_engine") - self.config = kwargs - self.rules: List[Callable] = [] - - # Initialize progress tracker - self.progress_tracker = get_progress_tracker() - - self.logger.debug("Validation engine initialized") - - def validate( - self, knowledge_graph: Any, rules: Optional[List[Callable]] = None - ) -> ValidationResult: - """ - Validate knowledge graph. - - This method validates a knowledge graph against a list of validation - rules. Rules can be provided as arguments or use the engine's stored - rules. Each rule should return a dict with "error" and/or "warning" - keys, or raise an exception. - - Args: - knowledge_graph: Knowledge graph instance to validate - rules: Optional list of validation rule functions (if None, uses - stored rules). Each rule should accept the knowledge graph - as argument and return a dict or raise an exception. - - Returns: - ValidationResult: Validation result containing: - - valid: True if no errors, False otherwise - - errors: List of error messages - - warnings: List of warning messages - - metadata: Additional validation metadata - """ - # Track validation - tracking_id = self.progress_tracker.start_tracking( - file=None, - module="kg_qa", - submodule="ValidationEngine", - message="Validating graph", - ) - - try: - rules_to_use = rules or self.rules - errors = [] - warnings = [] - - self.progress_tracker.update_tracking( - tracking_id, message=f"Validating with {len(rules_to_use)} rule(s)..." - ) - for rule in rules_to_use: - try: - result = rule(knowledge_graph) - if isinstance(result, dict): - if result.get("error"): - errors.append(result["error"]) - if result.get("warning"): - warnings.append(result["warning"]) - except Exception as e: - self.logger.error(f"Validation rule error: {e}") - errors.append(f"Validation rule failed: {e}") - - result = ValidationResult( - valid=len(errors) == 0, errors=errors, warnings=warnings - ) - self.progress_tracker.stop_tracking( - tracking_id, - status="completed", - message=f"Validation complete: {len(errors)} errors, {len(warnings)} warnings", - ) - return result - - except Exception as e: - self.progress_tracker.stop_tracking( - tracking_id, status="failed", message=str(e) - ) - raise - - def add_rule(self, rule: Callable) -> None: - """ - Add validation rule. - - This method adds a validation rule function to the engine's rule list. - The rule will be used in subsequent validate() calls. - - Args: - rule: Validation rule function (should accept knowledge graph and - return dict with "error"/"warning" keys or raise exception) - """ - self.rules.append(rule) - - def remove_rule(self, rule: Callable) -> None: - """ - Remove validation rule. - - This method removes a validation rule function from the engine's rule list. - - Args: - rule: Validation rule function to remove - """ - if rule in self.rules: - self.rules.remove(rule) - - -class RuleValidator: - """ - Rule-based validation engine. - - This class provides rule-based validation capabilities, enabling validation - against specific rule strings or identifiers. - - Features: - - Single rule validation - - Multiple rule validation - - Rule parsing and execution (planned) - - Example Usage: - >>> validator = RuleValidator() - >>> result = validator.validate_rule(knowledge_graph, "rule_name") - >>> results = validator.validate_all_rules(knowledge_graph, ["rule1", "rule2"]) - """ - - def __init__(self, **kwargs): - """ - Initialize rule validator. - - Sets up the validator with configuration options. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("rule_validator") - self.config = kwargs - - self.logger.debug("Rule validator initialized") - - def validate_rule(self, knowledge_graph: Any, rule: str) -> ValidationResult: - """ - Validate against a specific rule. - - This method validates a knowledge graph against a specific rule string - or identifier. Currently returns a placeholder result. In practice, - this would parse and execute the rule. - - Args: - knowledge_graph: Knowledge graph instance - rule: Rule string or identifier - - Returns: - ValidationResult: Validation result (currently placeholder) - """ - # In practice, this would parse and execute the rule - # For now, return a placeholder - return ValidationResult(valid=True) - - def validate_all_rules( - self, knowledge_graph: Any, rules: List[str] - ) -> Dict[str, ValidationResult]: - """ - Validate against multiple rules. - - This method validates a knowledge graph against multiple rules and - returns a dictionary mapping each rule name to its validation result. - - Args: - knowledge_graph: Knowledge graph instance - rules: List of rule strings or identifiers - - Returns: - dict: Dictionary mapping rule names to ValidationResult objects - """ - results = {} - for rule in rules: - results[rule] = self.validate_rule(knowledge_graph, rule) - - return results - - -class ConstraintValidator: - """ - Constraint-based validation engine. - - This class provides constraint-based validation capabilities, enabling - validation against schema constraints such as required properties, domain - and range constraints for relationships. - - Features: - - Entity constraint validation - - Relationship constraint validation - - Domain and range validation - - Example Usage: - >>> validator = ConstraintValidator() - >>> result = validator.validate_constraints(knowledge_graph, constraints) - """ - - def __init__(self, **kwargs): - """ - Initialize constraint validator. - - Sets up the validator with configuration options. - - Args: - **kwargs: Configuration options (currently unused) - """ - self.logger = get_logger("constraint_validator") - self.config = kwargs - - self.logger.debug("Constraint validator initialized") - - def validate_constraints( - self, knowledge_graph: Any, constraints: Dict[str, Any] - ) -> ValidationResult: - """ - Validate against constraints. - - This method validates a knowledge graph against schema constraints, - checking entity constraints (required properties) and relationship - constraints (domain and range). - - Args: - knowledge_graph: Knowledge graph instance - constraints: Constraints dictionary containing: - - entities: Dictionary mapping entity types to constraint dicts - with "required_props" list - - relationships: Dictionary mapping relationship types to - constraint dicts with "domain" and "range" - - Returns: - ValidationResult: Validation result with errors and warnings - """ - errors = [] - warnings = [] - - # Validate entity constraints - entity_constraints = constraints.get("entities", {}) - for entity_type, constraint in entity_constraints.items(): - required_props = constraint.get("required_props", []) - - # Check if entities of this type have required properties - # This is simplified - in practice would query the graph - if required_props: - warnings.append( - f"Entity type {entity_type} requires properties: {required_props}" - ) - - # Validate relationship constraints - rel_constraints = constraints.get("relationships", {}) - for rel_type, constraint in rel_constraints.items(): - domain = constraint.get("domain") - range_val = constraint.get("range") - - if domain and range_val: - # Check domain and range constraints - pass # Would validate in practice - - return ValidationResult( - valid=len(errors) == 0, errors=errors, warnings=warnings - )