Visualization¶

Comprehensive visualization suite for Knowledge Graphs, Ontologies, Embeddings, and Temporal data.

🎯 Overview¶

KG Visualization

Interactive network graphs with Force-directed, Hierarchical, and Circular layouts
Ontology View

Visualize class hierarchies, property domains/ranges, and taxonomy trees
Embedding Projector

2D/3D visualization of vector embeddings using UMAP, t-SNE, and PCA
Temporal Analysis

Timeline views and graph evolution visualization
Analytics Dashboards

Visual dashboards for centrality, community structure, and connectivity
Multi-Format Export

Export to HTML (interactive), PNG, SVG, PDF, and JSON

When to Use

Exploration: Interactively explore graph connections and clusters
Reporting: Generate static charts for reports and presentations
Debugging: Visually inspect graph structure and disconnected components
Analysis: Identify patterns, outliers, and trends in data

⚙️ Algorithms Used¶

Layout Algorithms¶

The visualization module uses various layout algorithms:

Force-Directed: Simulates physical forces (repulsion between nodes, springs for edges) to find equilibrium
Hierarchical: Tree-based layout for taxonomies and directed acyclic graphs (DAGs)
Circular: Arranges nodes in a circle, useful for analyzing interconnectivity
Community-Based: Groups nodes by community (Louvain/Leiden) and separates clusters

Dimensionality Reduction¶

The module supports multiple dimensionality reduction techniques:

UMAP: Uniform Manifold Approximation and Projection - Preserves global structure better than t-SNE
t-SNE: t-Distributed Stochastic Neighbor Embedding - Good for local clustering
PCA: Principal Component Analysis - Linear projection for variance maximization

Analytics Visualization¶

Centrality Sizing: Node size proportional to Degree/Betweenness/PageRank.
Heatmaps: Matrix visualization for adjacency or similarity.
Sankey Diagrams: Flow visualization for lineage or process steps.

Main Classes¶

KGVisualizer¶

Visualizes Knowledge Graph structure and communities.

Methods:

Method	Description
`visualize_network(graph)`	Standard network plot
`visualize_communities(graph)`	Color by community
`visualize_centrality(graph, centrality, centrality_type)`	Size/color by centrality
`visualize_entity_types(graph)`	Entity type distribution
`visualize_relationship_matrix(graph)`	Relationship frequency heatmap

Example:

from semantica.visualization import KGVisualizer

viz = KGVisualizer(layout="force", height=800)
fig = viz.visualize_network(kg, output="interactive")
fig.write_html("graph.html")

OntologyVisualizer¶

Visualizes schema and taxonomy.

Methods:

Method	Description
`visualize_hierarchy(ontology)`	Tree view of classes
`visualize_properties(ontology)`	Property domain/range graph
`visualize_structure(ontology)`	Class-property network
`visualize_class_property_matrix(ontology)`	Class vs property heatmap
`visualize_metrics(ontology)`	Metrics dashboard
`visualize_semantic_model(model)`	Visualize semantic model/network

EmbeddingVisualizer¶

Project high-dimensional vectors to 2D/3D.

Methods:

Method	Description	Algorithm
`visualize_2d_projection(embeddings, labels, method)`	2D Scatter plot	UMAP/t-SNE/PCA
`visualize_3d_projection(embeddings, labels, method)`	3D Scatter plot	UMAP/t-SNE/PCA
`visualize_similarity_heatmap(embeddings, labels)`	Pairwise similarity	Cosine
`visualize_clustering(embeddings, cluster_labels, method)`	Colored by cluster	UMAP/t-SNE/PCA
`visualize_multimodal_comparison(text_emb, image_emb, audio_emb)`	Compare modalities	UMAP/PCA

Example:

from semantica.visualization import EmbeddingVisualizer

viz = EmbeddingVisualizer()
viz.visualize_2d_projection(
    embeddings, 
    labels=labels, 
    method="umap",
    output="embeddings.html"
)

SemanticNetworkVisualizer¶

Visualizes semantic network structure and distributions.

Methods:

Method	Description
`visualize_network(semantic_network)`	Network visualization
`visualize_node_types(semantic_network)`	Node type distribution
`visualize_edge_types(semantic_network)`	Edge type distribution

AnalyticsVisualizer¶

Visualizes graph analytics results.

Methods:

Method	Description
`visualize_centrality_rankings(centrality, centrality_type, top_n)`	Top-k bar chart
`visualize_community_structure(graph, communities)`	Community network
`visualize_connectivity(connectivity)`	Components and sizes
`visualize_degree_distribution(graph)`	Degree histogram
`visualize_metrics_dashboard(metrics)`	Metrics dashboard
`visualize_centrality_comparison(centrality_results, top_n)`	Grouped comparison

TemporalVisualizer¶

Visualizes time-series and graph evolution.

Methods:

Method	Description
`visualize_timeline(events)`	Event timeline
`visualize_temporal_patterns(patterns)`	Pattern durations
`visualize_snapshot_comparison(snapshots)`	Compare snapshots
`visualize_version_history(version_history)`	Version timeline
`visualize_metrics_evolution(metrics_history, timestamps)`	Metrics over time

Convenience Functions¶

from semantica.visualization import (
    visualize_kg,
    visualize_embeddings,
    visualize_ontology,
    visualize_semantic_network,
    visualize_analytics,
    visualize_temporal,
    list_available_methods,
)

# One-line visualization
visualize_kg(kg, output="graph.html")
visualize_embeddings(embeddings, method="umap")
visualize_ontology(ontology, method="hierarchy")
visualize_semantic_network(semantic_network)
visualize_analytics({"centrality": centrality}, method="centrality")
visualize_temporal(temporal_data, method="timeline")
list_available_methods()

Configuration¶

Environment Variables¶

export VISUALIZATION_DEFAULT_LAYOUT=force
export VISUALIZATION_COLOR_SCHEME=vibrant
export VISUALIZATION_OUTPUT_FORMAT=interactive

YAML Configuration¶

visualization:
  layout:
    algorithm: force
    iterations: 50

  style:
    node_size: 10
    edge_width: 1
    color_scheme: "vibrant" # vibrant, pastel, dark

  export:
    width: 1200
    height: 800
    scale: 2.0

Integration Examples¶

Exploratory Data Analysis (EDA)¶

from semantica.ingest import Ingestor
from semantica.kg import KnowledgeGraph
from semantica.visualization import KGVisualizer, AnalyticsVisualizer

# 1. Load Data
kg = KnowledgeGraph.load("my_graph")

# 2. Visualize Structure
kg_viz = KGVisualizer()
kg_viz.visualize_network(kg, output="structure.html")

# 3. Visualize Analytics
analytics_viz = AnalyticsVisualizer()
centrality = {"rankings": [{"node": "A", "score": 0.9}, {"node": "B", "score": 0.7}]}
analytics_viz.visualize_centrality_rankings(centrality, centrality_type="degree", top_n=10, output="centrality.png")
analytics_viz.visualize_degree_distribution(kg, output="degree_dist.png")

Best Practices¶

Filter First: Don't try to visualize 1M nodes. Filter to a subgraph of <5000 nodes for readability.
Use Interactive: Interactive HTML plots (Plotly) allow zooming and hovering, which is essential for dense graphs.
Color Meaningfully: Use color to represent node types or communities, not just random assignment.
Size by Importance: Map node size to centrality (e.g., PageRank) to highlight important entities.

Cookbook¶

Interactive tutorials to learn graph visualization:

Visualization: Basic graph visualization techniques
Topics: Graph visualization, network diagrams, basic plotting
Difficulty: Beginner
Use Cases: Visualizing knowledge graphs, understanding graph structure
Complete Visualization Suite: Creating interactive, publication-ready visualizations
Topics: PyVis, NetworkX, D3.js, interactive visualizations, publication-ready graphics
Difficulty: Intermediate
Use Cases: Advanced visualizations, presentations, publications

Visualization¶

🎯 Overview¶

⚙️ Algorithms Used¶

Layout Algorithms¶

Dimensionality Reduction¶

Analytics Visualization¶

Main Classes¶

KGVisualizer¶

OntologyVisualizer¶

EmbeddingVisualizer¶

SemanticNetworkVisualizer¶

AnalyticsVisualizer¶

TemporalVisualizer¶

Convenience Functions¶

Configuration¶

Environment Variables¶

YAML Configuration¶

Integration Examples¶

Exploratory Data Analysis (EDA)¶

Best Practices¶

See Also¶

Cookbook¶