Visualization Module

semantica.visualization renders knowledge graphs, ontologies, embedding spaces, and temporal data as interactive HTML or static images: without launching the full Explorer server:

KGVisualizer: interactive network with force, hierarchical, and circular layouts
EmbeddingVisualizer: 2D/3D UMAP or t-SNE projections with cluster labels
TemporalVisualizer: timeline views and graph evolution across snapshots
AnalyticsVisualizer: centrality scores, community structure, degree distribution charts

Requires plotly: pip install plotly. Some exporters also need matplotlib or graphviz.

Exported Classes

Class	Role
`KGVisualizer`	Interactive network, community, and subgraph rendering with force/hierarchical/circular layouts
`OntologyVisualizer`	Class hierarchy and property relationship diagrams from any ontology
`EmbeddingVisualizer`	2D/3D UMAP or t-SNE projection of embedding spaces with cluster labels
`SemanticNetworkVisualizer`	Weighted semantic network rendering
`AnalyticsVisualizer`	Centrality scores, community structure, connectivity, and degree distribution charts
`TemporalVisualizer`	Timeline views and graph evolution across snapshots

Quick Start

Render a knowledge graph

from semantica.visualization import KGVisualizer

viz = KGVisualizer(layout="force", color_scheme="default")

# Interactive: opens in browser, supports hover and click
viz.visualize_network(graph, output="interactive")

Apply layout and color options

viz = KGVisualizer(layout="force", color_scheme="vibrant")

viz.visualize_network(
    graph,
    output="html",
    file_path="graph.html",
    node_color_by="type",      # color nodes by entity type attribute
)

Export to static formats

# Static PNG: for reports and embedding in documents
viz.visualize_network(graph, output="png", file_path="graph.png")

# Vector SVG: for publications and scalable diagrams
viz.visualize_network(graph, output="svg", file_path="graph.svg")

plotly is required for all visualizers. Install before use: pip install plotly. All visualizer methods raise ProcessingError if Plotly is not installed.

Visualizers

KGVisualizer
OntologyVisualizer
EmbeddingVisualizer
TemporalVisualizer
AnalyticsVisualizer

Interactive and static knowledge graph rendering:

from semantica.visualization import KGVisualizer

viz = KGVisualizer(layout="force", color_scheme="default")

# Interactive: opens in browser
viz.visualize_network(graph, output="interactive")

# Save as HTML file
viz.visualize_network(graph, output="html", file_path="graph.html")

# Static PNG
viz.visualize_network(graph, output="png", file_path="graph.png")

# Community-colored graph
viz.visualize_communities(graph, communities, file_path="communities.html")

# Centrality-sized nodes
viz.visualize_centrality(graph, centrality, centrality_type="degree")

# Entity type distribution bar chart
viz.visualize_entity_types(graph, output="interactive")

# Relationship frequency heatmap
viz.visualize_relationship_matrix(graph, output="interactive")

Use max_nodes for large graphs. Force-directed layouts become unreadable and slow above ~1,000 nodes. Filter to a subgraph before visualizing large graphs.

HTML output is always the best starting point. Interactive HTML lets you zoom, pan, and hover for details. Only export to PNG/SVG/PDF when embedding in a report.

For interactive dashboards, prefer Explorer. KGVisualizer.visualize_network() generates a self-contained HTML file. The Explorer CLI (semantica-explorer) gives a full live web app with search, filtering, path-finding, and REST API.

Layout options (layout=):

Layout	Description	Best For
`force`	Physics simulation: clusters emerge naturally	General graphs
`hierarchical`	Top-down tree layout	Taxonomies, org charts
`circular`	Nodes on a circle, edges as chords	Small dense graphs

Visualize class hierarchies and property relationships:

from semantica.visualization import OntologyVisualizer

viz = OntologyVisualizer()

# Class hierarchy tree
viz.visualize_hierarchy(ontology, output="interactive")

# Property domain/range graph
viz.visualize_properties(ontology, output="html", file_path="properties.html")

# Full structure network (classes + properties)
viz.visualize_structure(ontology, output="interactive")

# Class-property matrix heatmap
viz.visualize_class_property_matrix(ontology, output="html", file_path="matrix.html")

# Ontology metrics dashboard
viz.visualize_metrics(ontology, output="interactive")

Project high-dimensional embeddings into 2D for cluster analysis:

from semantica.visualization import EmbeddingVisualizer

viz = EmbeddingVisualizer()

viz.visualize_2d_projection(
    embeddings=embeddings,
    labels=labels,
    output="interactive",
    file_path="embeddings.html",
    method="umap",    # "umap" | "tsne" | "pca"
)

Method	Speed	Preserves	Best For
`umap`	Fast	Global + local structure	Large datasets, cluster discovery
`tsne`	Medium	Local structure	Tight cluster separation
`pca`	Very fast	Variance	Quick overview, linear structure

UMAP is faster than t-SNE at scale. For embedding spaces with >5,000 points, UMAP completes in seconds; t-SNE may take minutes. Both produce good cluster separation.

Visualize how a knowledge graph changes over time:

from semantica.visualization import TemporalVisualizer

viz = TemporalVisualizer()

# Timeline of entity/relationship changes
viz.visualize_timeline(temporal_data, output="interactive")

# Animated network evolution: one frame per time step
viz.visualize_network_evolution(temporal_kg, output="html", file_path="evolution.html")

# Side-by-side snapshot comparison
# snapshots: dict mapping timestamp strings to graph dicts
snapshots = {
    "2023-01": graph_v1,
    "2024-01": graph_v2,
}
viz.visualize_snapshot_comparison(snapshots, output="html", file_path="diff.html")

# Temporal patterns: pass a list of pattern dicts
viz.visualize_temporal_patterns(patterns, output="html", file_path="patterns.html")

# Metrics evolution over time
viz.visualize_metrics_evolution(metrics_history, timestamps, output="interactive")

Visualize graph analytics results: centrality, communities, and degree distribution:

from semantica.visualization import AnalyticsVisualizer

viz = AnalyticsVisualizer()

# Bar chart of top-N nodes by centrality measure
# param is centrality_type= (not metric=) and top_n= (not top_k=)
viz.visualize_centrality_rankings(
    centrality,
    centrality_type="pagerank",
    top_n=20,
    output="html",
    file_path="centrality.html",
)

# Community-colored network graph
viz.visualize_community_structure(kg, communities, output="html", file_path="communities.html")

# Degree distribution histogram
viz.visualize_degree_distribution(kg, output="html", file_path="degree_dist.html")

# Connectivity analysis (connected/disconnected, component sizes)
viz.visualize_connectivity(connectivity, output="interactive")

# Full metrics dashboard (nodes, edges, density, diameter)
viz.visualize_metrics_dashboard(metrics, output="interactive")

# Compare multiple centrality measures side-by-side
viz.visualize_centrality_comparison(centrality_results, top_n=10)

Color Schemes

All visualizers accept a color_scheme= constructor parameter:

viz = KGVisualizer(color_scheme="vibrant")

Scheme	Description	Best For
`default`	Blue-grey palette	General use
`vibrant`	High-contrast, saturated colours	Presentations
`pastel`	Soft, muted tones	Light backgrounds
`dark`	Dark background with bright nodes	Dark-mode dashboards
`light`	White background, thin edges	Publications, print
`colorblind`	Okabe-Ito safe palette	Accessibility

Use color_scheme="colorblind" in publications and dashboards. The Okabe-Ito palette is readable for everyone, including the ~8% of readers who are red-green colorblind.

Export Formats

Format	Interactive	Scalable	Best For
`.html`	Yes	N/A	Web dashboards, exploratory analysis
`.png`	No	No	Reports, Jupyter notebooks
`.svg`	No	Yes	Publications, slide decks
`.pdf`	No	Yes	Print, compliance exports

Convenience Functions

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

# Returns Plotly figure or None
fig = visualize_kg(graph, output="interactive", method="default")
fig = visualize_ontology(ontology, output="interactive", method="hierarchy")
fig = visualize_embeddings(embeddings, labels, output="interactive", method="2d_projection")
fig = visualize_analytics(analytics_data, output="interactive", method="centrality")
fig = visualize_temporal(temporal_data, output="interactive", method="timeline")

Graph Explorer (Full Dashboard)

For a full browser-based UI with search, path finding, and the Ontology Hub, launch the Explorer CLI:

semantica-explorer --graph my_graph.json

See the Explorer reference for the full feature set and REST API.

Knowledge Graph

The graph being visualized.

Ontology

Visualize ontology class structure.

Embeddings

Generate the embeddings visualized here.

Explorer

Full interactive Knowledge Explorer UI.

​Exported Classes

​Quick Start

​Visualizers

​Color Schemes

​Export Formats

​Convenience Functions

​Graph Explorer (Full Dashboard)

Knowledge Graph

Ontology

Embeddings

Explorer

Exported Classes

Quick Start

Visualizers

Color Schemes

Export Formats

Convenience Functions

Graph Explorer (Full Dashboard)