ontoink 0.4.0

Interactive ontology visualization, SHACL validation, and live TTL editing plugin for MkDocs

ontoink

Interactive ontology visualization, SHACL validation, and live TTL editing for MkDocs.

ontoink is a MkDocs plugin that transforms RDF/Turtle files into interactive, publication-ready ontology diagrams with SHACL constraint visualization. Write a simple code block in your markdown, and ontoink generates a fully interactive graph.

Live Demo

Features

Interactive Graph Visualization

• Formal ontology notation with distinct shapes per element type
• Multiple layouts: dagre (hierarchical), force-directed, circle, concentric, tree, grid
• Pan, zoom, fullscreen, fuzzy search with highlight
• Color-coded by ontology source (BFO, IAO, FOAF, Schema.org, etc.)
• Minimap for navigation on large graphs
• Auto-focus on most-connected node for large ontologies (>30 nodes)

Visual Notation

Element	Shape	Default Color
Class	Rectangle (solid border)	By ontology source
Individual	Ellipse	#E6E6E6
Literal	Ellipse (dashed border)	#93D053
Object Property	Blue solid line, filled arrow	#2563eb
Data Property	Green line, hollow arrow	#16a34a
rdf:type	Grey dashed line	#9ca3af
rdfs:subClassOf	Black solid line	#374151
SHACL Constraint	Cyan dashed bold line	#0891b2

Click Popups

• Node label, type badge, full IRI (clickable)
• Copy Label / Copy IRI buttons
• Ontology source indicator
• Connected edges (incoming/outgoing)
• SHACL constraints applicable to the node

SHACL Constraint Overlay

• Constraints shown as bold dashed cyan edges with cardinality badges [1..*]
• Build-time validation using pySHACL
• Violations highlighted in the validation panel

Inline TTL Editor + Live Validation

• Expandable editor panel below each diagram
• CodeMirror with Turtle syntax highlighting
• Edit TTL data and validate against SHACL shapes in real-time
• Update the graph live from edited TTL
• Reset to original data

Export Preview + Publication-Ready Output

• PNG export — clicking PNG/SVG opens a preview dialog showing the final image
• Toggle options: include/exclude legend and namespace prefixes
• High-DPI (3x scale) with white background
• Legend rendered in a clean rounded box with proper shape icons and edge arrows
• Namespace prefixes shown as compact tags below the legend
• SVG export — vector graphics for papers and presentations
• TTL download — download the current (possibly edited) TTL data

Edit Layout

• Edit Layout button opens a panel to customize the visualization
• Change colors per node type, edge type, and namespace
• Change node shapes (rectangle, ellipse, diamond, hexagon, triangle, star, etc.)
• Change edge line styles (solid, dashed, dotted) and arrow shapes
• Toggle prefix visibility

OWL Reasoning

• Reasoning button shows inferred triples from OWL DL reasoning
• Powered by HermiT (via owlready2), with owlrl fallback
• Infers subclass chains, inverse properties, transitive closures, symmetric relations
• "Show on graph" checkbox overlays inferred triples as purple dotted edges/nodes
• "Validate with Inferences" runs SHACL validation including inferred triples
• Smart filtering removes reflexive triples and built-in namespace noise

Smart Namespace Legend

• Only active prefixes (used in the graph) shown by default
• "Show all" toggle reveals all declared prefixes
• Prefixes displayed as compact styled tags, separated from the main legend

Graph Analytics

• Statistics panel with ontology metrics: class/property counts, hierarchy depth, triple count
• Consistency check via HermiT — green/red badge showing ontology consistency
• SHACL coverage map — color-codes classes by validation coverage
• LOD Cloud links — auto-detects linked open data sources (DBpedia, Wikidata, Schema.org, etc.)
• Path finder — Dijkstra shortest path between any two nodes with animated visualization
• SPARQL queries — run SELECT queries on the loaded graph, highlight results

OntoSniff — Ontology Quality Checker

• 9 anti-pattern detectors based on published research (Poveda-Villalón et al. 2014, Rector et al. 2004, Gangemi et al. 2006)
• Detects: lazy classes, missing labels, missing domain/range, singleton hierarchies, property soup, orphan classes, missing inverse, no SHACL coverage, label language gaps
• Quality score (0-100) with severity breakdown
• Integrated into the Stats panel for every diagram + standalone OntoSniff page
• Shareable via URL: ?source=https://example.org/ontology.ttl

Browser-only Tools (no installation)

• Playground — paste or upload TTL and visualize instantly. Shareable via URL parameters.
• SHACL Editor — build SHACL shapes visually with templates, tooltips, live preview, and Shape Recommender that auto-generates constraints from instance data or SPARQL endpoints using data profiling (Mihindukulasooriya et al. 2018).
• SPARQL Explorer — connect to any SPARQL endpoint, auto-discover schema, write queries with Ctrl+Space autocomplete. Adaptive discovery for large KGs (DBpedia, Wikidata).
• OntoSniff — paste TTL and get instant quality analysis with a score and annotated anti-patterns.
• Abstract Model View — toggle between full graph and schema-only view (classes and class-level properties only).

Architecture & Design Decisions

Why ontoink?

Existing ontology visualization tools either require complex desktop installations (Protégé), produce static non-interactive diagrams (WebVOWL), or don't integrate with documentation workflows. ontoink fills this gap:

• Documentation-first: embeds directly in MkDocs, the standard for Python project documentation
• Interactive by default: every diagram is explorable — click, search, zoom, export
• Verifiable: SHACL validation is built in, not an afterthought
• Publication-ready: export PNG/SVG with legend and prefixes for papers and presentations

Adaptive Schema Discovery (SPARQL Explorer)

Connecting to a SPARQL endpoint with millions of triples (DBpedia: 9.5B, Wikidata: 17B) requires careful query design:

1. Fast probe — SELECT * WHERE { ?s ?p ?o } LIMIT 1 with 10s timeout verifies the endpoint is reachable and supports CORS
2. Class discovery with counts — SELECT ?class (COUNT(?inst) AS ?count) GROUP BY ?class LIMIT 100 gives the schema overview. If this times out (>15s), falls back to SELECT DISTINCT ?class which is orders of magnitude faster
3. Property discovery with domain/range — joins ?s a ?domain . ?o a ?range to infer property signatures. Falls back to SELECT DISTINCT ?prop for large endpoints
4. Batch label fetching — VALUES clause retrieves labels for up to 80 IRIs in one query

This adaptive approach means any endpoint works — from a 100-triple demo to Wikidata.

OWL Reasoning Pipeline

ontoink uses a two-stage reasoning approach:

1. Primary: HermiT (via owlready2) — full OWL DL tableau reasoner. Handles class hierarchy completion, inverse/transitive/symmetric property inference, consistency checking. Runs at MkDocs build time.
2. Fallback: owlrl — OWL-RL profile for environments without Java. Provides rdfs:subClassOf and basic property inference.
3. Smart filtering — removes reflexive triples (x sameAs x), built-in namespace noise (XSD, OWL, RDF, RDFS), and domain/range propagation to show only meaningful inferences.

SHACL Shape Recommendation

The Shape Recommender uses data profiling to auto-generate SHACL constraints:

1. Class discovery — identifies all classes with instances (rdf:type)
2. Property profiling — for each class, counts property usage across all instances
3. Constraint inference:
   • If 90%+ of instances have property P → sh:minCount 1 (mandatory)
   • If no instance has >1 value → sh:maxCount 1 (functional)
   • If all values are same XSD type → sh:datatype
   • If all values are IRIs → sh:nodeKind sh:IRI
4. Confidence scoring — percentage of instances exhibiting the pattern

Based on: Mihindukulasooriya et al. (2018) "RDF Shape Induction using Knowledge Base Profiling".

Novel extensions beyond Mihindukulasooriya (2018):

Feature	Original paper	ontoink extension
Cardinality	min/max count	Same
Datatype	XSD detection	Same
sh:class	Not covered	Infers target class from IRI value types
sh:pattern	Not covered	Auto-detects email, URL, uppercase patterns
sh:minLength/maxLength	Not covered	String length statistics
sh:minInclusive/maxInclusive	Not covered	Numeric range constraints
Uniqueness	Not covered	Detects potential identifiers
Confidence	Binary	Percentage-based (0-100%)

Works from uploaded TTL data or directly from SPARQL endpoints. Navigate shapes with Prev/Next, edit, download individually, or accept into the visual editor.

Client-side SPARQL Autocomplete

The SPARQL query editor provides Wikidata-style autocomplete:

• Ctrl+Space triggers the popup
• Fuzzy matching searches across IRI, label, and prefixed name simultaneously
• Classes, properties, and SPARQL keywords are all suggested with color-coded type badges
• Selecting an item inserts the full <IRI> into the query

Installation

pip install ontoink

Or install from source:

pip install git+https://github.com/ISE-FIZKarlsruhe/ontoink.git

Usage

1. Add to mkdocs.yml

plugins:
  - search
  - ontoink

markdown_extensions:
  - pymdownx.superfences:
      preserve_tabs: true

2. Write ontoink blocks in markdown

```ontoink
source: path/to/instance-data.ttl
shape: path/to/shacl-shape.ttl
```

3. Build or serve

mkdocs serve

Configuration Options

Option	Default	Description
source	(required)	Path to TTL data file (relative to docs/)
shape	(optional)	Path to SHACL shape file
height	500px	Height of the graph canvas
editor	true	Show the "Edit & Validate" button
legend	true	Show the legend panel
namespaces	true	Show namespace prefixes

Full example

```ontoink
source: shapes/foaf-person/shape-data.ttl
shape: shapes/foaf-person/shape.ttl
height: 600px
editor: true
legend: true
namespaces: true
```

How It Works

1. Build time (Python): The plugin parses your TTL files with rdflib, classifies nodes (Class/Individual/Literal), resolves labels, detects ontology sources for color coding, extracts SHACL constraints, and runs pySHACL validation. The result is serialized as JSON and embedded in the HTML.

2. Browser (JavaScript): Cytoscape.js renders the graph with dagre layout. CodeMirror provides the TTL editor. A lightweight JavaScript SHACL checker enables live validation without server round-trips.

Development

git clone https://github.com/ISE-FIZKarlsruhe/ontoink.git
cd ontoink
pip install -e ".[dev]"
pytest -v

Demo site

cd demo
mkdocs serve

Requirements

• Python >= 3.9
• MkDocs >= 1.4
• rdflib >= 6.0
• pySHACL >= 0.25.0
• pymdown-extensions >= 10.0

Browser-side dependencies are loaded from CDN (no npm/bundling needed):

• Cytoscape.js, cytoscape-dagre, cytoscape-svg
• CodeMirror 5 with Turtle mode

Contributing

Contributions are welcome. Please open an issue first to discuss proposed changes.

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure pytest passes
5. Submit a pull request

License

MIT License. See LICENSE.

Author

Ebrahim Norouzi — FIZ Karlsruhe, ISE

Acknowledgments

• Developed in the context of NFDI and NFDI-MatWerk
• Visual notation inspired by ontology diagramming best practices and formal OWL notation
• Interactive visualization powered by Cytoscape.js