semantica/cookbook/introduction/16_Visualization.ipynb

Visualization

Overview

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.

Documentation: API Reference

Learning Objectives

• Use KGVisualizer to visualize knowledge graphs
• Use OntologyVisualizer to visualize ontologies
• Use EmbeddingVisualizer to visualize embeddings

Installation

Install Semantica from PyPI:

pip install semantica
# Or with all optional dependencies:
pip install semantica[all]

---

Step 1: Knowledge Graph Visualization

Visualize knowledge graphs.

In [ ]:

from semantica.visualization import KGVisualizer
from semantica.kg import GraphBuilder

kg_visualizer = KGVisualizer()
builder = GraphBuilder()

entities = [
    {"id": "e1", "type": "Organization", "name": "Apple Inc.", "properties": {}},
    {"id": "e2", "type": "Person", "name": "Tim Cook", "properties": {}}
]

relationships = [
    {"source": "e2", "target": "e1", "type": "CEO_of", "properties": {}}
]

kg = builder.build(entities, relationships)

visualization = kg_visualizer.visualize_network(kg, output="interactive")

Step 2: Ontology Visualization

Visualize ontologies.

In [ ]:

from semantica.visualization import OntologyVisualizer
from semantica.ontology import OntologyGenerator

ontology_visualizer = OntologyVisualizer()
generator = OntologyGenerator()

ontology = generator.generate(entities, relationships)

visualization = ontology_visualizer.visualize_hierarchy(ontology, output="interactive")

Step 3: Embedding Visualization

Visualize embeddings.

In [ ]:

from semantica.visualization import EmbeddingVisualizer
from semantica.embeddings import EmbeddingGenerator
import numpy as np

embedding_visualizer = EmbeddingVisualizer()
generator = EmbeddingGenerator()

texts = ["Apple Inc.", "Microsoft Corporation", "Amazon"]
embeddings = generator.generate_embeddings(texts, data_type="text")
labels = ["Apple", "Microsoft", "Amazon"]

visualization = embedding_visualizer.visualize_2d_projection(embeddings, labels, method="umap")

Step 4: Semantic Network Visualization

Visualize semantic networks: structure, node types, and edge types.

In [ ]:

from semantica.visualization import SemanticNetworkVisualizer

semantic_network = {
    "nodes": [
        {"id": "n1", "label": "Node 1", "type": "Entity"},
        {"id": "n2", "label": "Node 2", "type": "Entity"}
    ],
    "edges": [
        {"source": "n1", "target": "n2", "label": "related_to"}
    ]
}

sem_viz = SemanticNetworkVisualizer()
sem_viz.visualize_network(semantic_network, output="interactive")
sem_viz.visualize_node_types(semantic_network, output="interactive")
sem_viz.visualize_edge_types(semantic_network, output="interactive")

Step 5: Advanced Embedding Visualization

Compare multi-modal embeddings and assess embedding quality.

In [ ]:

import numpy as np
from semantica.visualization import EmbeddingVisualizer

# Synthetic multi-modal embeddings (text, image, audio)
text_emb = np.random.rand(50, 128)
image_emb = np.random.rand(50, 128)
audio_emb = np.random.rand(50, 128)

emb_viz = EmbeddingVisualizer()
emb_viz.visualize_multimodal_comparison(text_emb, image_emb, audio_emb, output="interactive")

# Embedding quality metrics
quality_fig = emb_viz.visualize_quality_metrics(text_emb, output="interactive")

Summary

You've learned how to visualize data:

• KGVisualizer: Visualize knowledge graphs
• OntologyVisualizer: Visualize ontologies
• EmbeddingVisualizer: Visualize embeddings, multi-modal and quality metrics
• SemanticNetworkVisualizer: Visualize semantic network structure and type distributions

Next: Learn how to detect conflicts in the Conflict_Detection notebook.