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.

Context & Intelligence

Storage

Data Pipeline

Quality & Provenance

Output

Utilities

Visualization Module

Exported Classes

Quick Start

Visualizers

Color Schemes

Export Formats

Convenience Functions

Graph Explorer (Full Dashboard)

​Exported Classes

​Quick Start

​Visualizers

​Color Schemes

​Export Formats

​Convenience Functions

​Graph Explorer (Full Dashboard)

Exported Classes

Quick Start

Visualizers

Color Schemes

Export Formats

Convenience Functions

Graph Explorer (Full Dashboard)