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 [ ]:

!pip install -q semantica

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()
# Initialize generator with min_occurrences=1 to allow single-instance classes
generator = OntologyGenerator(min_occurrences=1)

# Generate ontology using the correct method signature (dictionary input)
ontology = generator.generate_ontology({"entities": entities, "relationships": relationships})

# Visualize the hierarchy
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

# Initialize (uses new defaults: Vibrant colors, Kamada-Kawai layout)
viz = SemanticNetworkVisualizer()

# Your semantic network data
semantic_network = {
    "nodes": [
        {"id": "n1", "label": "Python", "type": "Language"},
        {"id": "n2", "label": "Code", "type": "Concept"}
    ],
    "edges": [
        {"source": "n1", "target": "n2", "label": "writes"}
    ]
}

# This will now display the interactive graph in the notebook cell
viz.visualize_network(
    semantic_network, 
    output="html", 
    file_path="network_graph.html"
)

Summary

You've learned how to visualize data:

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

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