{
 "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": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/introduction/12_Graph_Quality.ipynb)\n",
    "\n",
    "# Graph Quality\n",
    "\n",
    "## Overview\n",
    "\n",
    "This notebook demonstrates how to assess and improve knowledge graph quality using Semantica's quality assurance modules. You'll learn to use `KGQualityAssessor`, `ConsistencyChecker`, `CompletenessValidator`, and `QualityMetrics`.\n",
    "\n",
    "\n",
    "**Documentation**: [API Reference](https://semantica.readthedocs.io/reference/kg_qa/)\n",
    "\n",
    "### Learning Objectives\n",
    "\n",
    "- Use `KGQualityAssessor` for overall quality assessment\n",
    "- Use `ConsistencyChecker` to validate consistency\n",
    "- Use `CompletenessValidator` to check completeness\n",
    "- Use `QualityMetrics` to calculate quality metrics\n",
    "\n",
    "## Installation\n",
    "\n",
    "Install Semantica from PyPI:\n",
    "\n",
    "```bash\n",
    "pip install semantica\n",
    "# Or with all optional dependencies:\n",
    "pip install semantica[all]\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "## Step 1: Quality Assessment\n",
    "\n",
    "Assess overall graph quality.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    
    "from semantica.kg import GraphBuilder\n",
    "\n",
    "builder = GraphBuilder()\n",
    
    "\n",
    "entities = [\n",
    "    {\"id\": \"e1\", \"type\": \"Organization\", \"name\": \"Apple Inc.\", \"properties\": {}}\n",
    "]\n",
    "\n",
    "relationships = []\n",
    "\n",
    "kg = builder.build(entities, relationships)\n",
    "\n",
    "quality_score = assessor.assess_overall_quality(kg)\n",
    "\n",
    "print(f\"Overall quality score: {quality_score.get('overall_score', 0):.3f}\")\n",
    "print(f\"Completeness: {quality_score.get('completeness', 0):.3f}\")\n",
    "print(f\"Consistency: {quality_score.get('consistency', 0):.3f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Consistency Checking\n",
    "\n",
    "Check graph consistency.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    
    "\n",
    "consistency_checker = ConsistencyChecker()\n",
    "\n",
    "consistency_result = consistency_checker.check_consistency(kg)\n",
    "\n",
    "print(f\"Consistency check:\")\n",
    "print(f\"  Is consistent: {consistency_result.get('is_consistent', False)}\")\n",
    "print(f\"  Issues: {len(consistency_result.get('issues', []))}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Completeness Validation\n",
    "\n",
    "Validate graph completeness.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    
    "\n",
    "completeness_validator = CompletenessValidator()\n",
    "\n",
    "completeness_result = completeness_validator.validate_completeness(kg)\n",
    "\n",
    "print(f\"Completeness validation:\")\n",
    "print(f\"  Is complete: {completeness_result.get('is_complete', False)}\")\n",
    "print(f\"  Missing properties: {len(completeness_result.get('missing_properties', []))}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Quality Metrics\n",
    "\n",
    "Calculate detailed quality metrics.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    
    "\n",
    "quality_metrics = QualityMetrics()\n",
    "\n",
    "metrics = quality_metrics.calculate_metrics(kg)\n",
    "\n",
    "print(f\"Quality metrics:\")\n",
    "print(f\"  Entity coverage: {metrics.get('entity_coverage', 0):.3f}\")\n",
    "print(f\"  Relationship coverage: {metrics.get('relationship_coverage', 0):.3f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "You've learned how to assess graph quality:\n",
    "\n",
    "- **KGQualityAssessor**: Overall quality assessment\n",
    "- **ConsistencyChecker**: Consistency validation\n",
    "- **CompletenessValidator**: Completeness validation\n",
    "- **QualityMetrics**: Detailed quality metrics\n",
    "\n",
    "Next: Learn how to deduplicate entities in the Deduplication notebook.\n"
   ]
  }
 ],
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