{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Visualization\n",
        "\n",
        "## Overview\n",
        "\n",
        "This notebook demonstrates how to visualize knowledge graphs, ontologies, and embeddings using Semantica's visualization modules. You'll learn to use `KGVisualizer`, `OntologyVisualizer`, and `EmbeddingVisualizer`.\n",
        "\n",
        "### Learning Objectives\n",
        "\n",
        "- Use `KGVisualizer` to visualize knowledge graphs\n",
        "- Use `OntologyVisualizer` to visualize ontologies\n",
        "- Use `EmbeddingVisualizer` to visualize embeddings\n",
        "\n",
        "---\n",
        "\n",
        "## Step 1: Knowledge Graph Visualization\n",
        "\n",
        "Visualize knowledge graphs.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from semantica.visualization import KGVisualizer\n",
        "from semantica.kg import GraphBuilder\n",
        "\n",
        "kg_visualizer = KGVisualizer()\n",
        "builder = GraphBuilder()\n",
        "\n",
        "entities = [\n",
        "    {\"id\": \"e1\", \"type\": \"Organization\", \"name\": \"Apple Inc.\", \"properties\": {}},\n",
        "    {\"id\": \"e2\", \"type\": \"Person\", \"name\": \"Tim Cook\", \"properties\": {}}\n",
        "]\n",
        "\n",
        "relationships = [\n",
        "    {\"source\": \"e2\", \"target\": \"e1\", \"type\": \"CEO_of\", \"properties\": {}}\n",
        "]\n",
        "\n",
        "kg = builder.build(entities, relationships)\n",
        "\n",
        "visualization = kg_visualizer.visualize_network(kg, output=\"interactive\")\n",
        "\n",
        "print(\"Generated knowledge graph visualization\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Step 2: Ontology Visualization\n",
        "\n",
        "Visualize ontologies.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from semantica.visualization import OntologyVisualizer\n",
        "from semantica.ontology import OntologyGenerator\n",
        "\n",
        "ontology_visualizer = OntologyVisualizer()\n",
        "generator = OntologyGenerator()\n",
        "\n",
        "ontology = generator.generate(entities, relationships)\n",
        "\n",
        "visualization = ontology_visualizer.visualize_hierarchy(ontology, output=\"interactive\")\n",
        "\n",
        "print(\"Generated ontology visualization\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Step 3: Embedding Visualization\n",
        "\n",
        "Visualize embeddings.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from semantica.visualization import EmbeddingVisualizer\n",
        "from semantica.embeddings import EmbeddingGenerator\n",
        "import numpy as np\n",
        "\n",
        "embedding_visualizer = EmbeddingVisualizer()\n",
        "generator = EmbeddingGenerator()\n",
        "\n",
        "texts = [\"Apple Inc.\", \"Microsoft Corporation\", \"Amazon\"]\n",
        "embeddings = generator.generate(texts)\n",
        "labels = [\"Apple\", \"Microsoft\", \"Amazon\"]\n",
        "\n",
        "visualization = embedding_visualizer.visualize_2d_projection(embeddings, labels, method=\"umap\")\n",
        "\n",
        "print(\"Generated embedding visualization\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Summary\n",
        "\n",
        "You've learned how to visualize data:\n",
        "\n",
        "- **KGVisualizer**: Visualize knowledge graphs\n",
        "- **OntologyVisualizer**: Visualize ontologies\n",
        "- **EmbeddingVisualizer**: Visualize embeddings\n",
        "\n",
        "Next: Learn how to detect conflicts in the Conflict_Detection notebook.\n"
      ]
    }
  ],
  "metadata": {
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}