Tags Taxonomy Visualization

Overview

AIWebFeeds provides a comprehensive tags taxonomy that organizes AI/ML topics into a hierarchical ontology. This system supports:

Hierarchical relationships (parent/child)
Semantic relations (depends_on, implements, influences, etc.)
Facet classification (domain, task, methodology, etc.)
Multiple visualization formats (Mermaid, JSON graphs, DOT)

Taxonomy Structure

The taxonomy is defined in /data/topics.yaml and includes:

~100+ topics across AI/ML domains
4 facet groups: conceptual, technical, contextual, communicative
Directed relations: depends_on, implements, influences
Symmetric relations: related_to, same_as, contrasts_with

Example Topic

- id: llm
  label: Large Language Models
  facet: task
  facet_group: conceptual
  parents: [genai, nlp]
  relations:
    depends_on: [training, data]
    influences: [product, education]
    related_to: [agents, evaluation]
  rank_hint: 0.99

Visualization Methods

1. CLI Visualization

Generate Mermaid diagrams, JSON graphs, or view statistics:

# Generate Mermaid diagram
aiwebfeeds visualize mermaid -o taxonomy.mermaid

# With options
aiwebfeeds visualize mermaid \
  --direction LR \
  --max-depth 3 \
  --facets "domain,task" \
  --no-relations

# Generate JSON graph for D3.js/visualization libraries
aiwebfeeds visualize json -o taxonomy.json

# View statistics
aiwebfeeds visualize stats

2. Python API

Use the taxonomy module programmatically:

from ai_web_feeds.taxonomy import load_taxonomy, TaxonomyVisualizer

# Load taxonomy
taxonomy = load_taxonomy()

# Create visualizer
visualizer = TaxonomyVisualizer(taxonomy)

# Generate Mermaid diagram
mermaid_code = visualizer.to_mermaid(
    direction="TD",
    max_depth=3,
    include_relations=True
)

# Get JSON graph for D3.js
graph = visualizer.to_json_graph()
print(f"Nodes: {len(graph['nodes'])}, Links: {len(graph['links'])}")

# Get statistics
stats = visualizer.get_statistics()
print(f"Total topics: {stats['total_topics']}")
print(f"Max depth: {stats['max_depth']}")

3. Interactive Mermaid Diagram

Below is an interactive visualization of the core AI/ML taxonomy (depth=2):

Topics are organized into four facet groups with distinct visual styling:

Conceptual

Core AI/ML concepts, domains, and tasks

Technical

Infrastructure, tools, and technical components

Contextual

Industry, governance, and application domains

Communicative

Media types and communication channels

Use Cases

Feed Categorization

Topics are used to categorize and filter RSS/Atom feeds:

from ai_web_feeds.taxonomy import load_taxonomy

taxonomy = load_taxonomy()

# Get all LLM-related topics
llm_topic = taxonomy.get_topic("llm")
llm_children = taxonomy.get_children("llm")

# Filter feeds by topic
conceptual_topics = taxonomy.get_topics_by_facet_group("conceptual")

Recommendation Systems

Use the taxonomy for content recommendations:

# Find related topics
topic = taxonomy.get_topic("llm")
related = topic.relations.get("related_to", [])

# Get topic dependencies
dependencies = topic.relations.get("depends_on", [])

Analytics & Insights

Generate insights about your feed collection:

visualizer = TaxonomyVisualizer(taxonomy)
stats = visualizer.get_statistics()

print(f"Facet distribution: {stats['facets']}")
print(f"Average depth: {stats['avg_depth']:.2f}")

Advanced Features

Filtering by Depth

Visualize only top-level topics:

mermaid_code = visualizer.to_mermaid(max_depth=2)

Focus on specific topic types:

mermaid_code = visualizer.to_mermaid(
    filter_facets=["domain", "task"]
)

Custom Styling

The Mermaid diagrams include custom CSS classes based on facet groups, which you can override in your rendering environment.

Data Format

The taxonomy follows a strict JSON Schema (see /data/topics.schema.json):

{
  "id": "string (kebab-case)",
  "label": "Human-readable name",
  "facet": "Category type",
  "facet_group": "conceptual | technical | contextual | communicative",
  "parents": ["parent-topic-ids"],
  "relations": {
    "depends_on": ["topic-ids"],
    "implements": ["topic-ids"],
    "influences": ["topic-ids"]
  },
  "rank_hint": 0.0-1.0
}

Export Formats

Mermaid

Best for documentation and GitHub/GitLab READMEs.

JSON Graph

Compatible with D3.js, Cytoscape.js, and other graph visualization libraries:

{
  "nodes": [
    {
      "id": "ai",
      "label": "Artificial Intelligence",
      "facet": "domain",
      "facet_group": "conceptual"
    }
  ],
  "links": [
    {
      "source": "ai",
      "target": "ml",
      "type": "parent"
    }
  ]
}

DOT (Graphviz)

For high-quality static diagrams (requires Graphviz):

# Generate DOT file
python -c "
from ai_web_feeds.taxonomy import load_taxonomy, TaxonomyVisualizer
viz = TaxonomyVisualizer(load_taxonomy())
print(viz.to_dot())
" > taxonomy.dot

# Render with Graphviz
dot -Tpng taxonomy.dot -o taxonomy.png

Contributing

To add or modify topics:

Edit /data/topics.yaml
Validate against /data/topics.schema.json
Run aiwebfeeds validate data/topics.yaml
Generate updated visualizations
Submit a pull request

API Reference

See the Python API documentation for complete details on:

TopicNode - Topic model
TopicsTaxonomy - Taxonomy container
TaxonomyVisualizer - Visualization generator
load_taxonomy() - Load from YAML
export_mermaid() - Export Mermaid diagram
export_json_graph() - Export JSON graph