An interactive, domain-independent learning platform.
Knowledge Explorer transforms complex subjects into visual, navigable knowledge maps. Users can explore topics, understand relationships between concepts, read plain-language summaries, and learn through structured knowledge rather than disconnected content.
The platform itself is domain-independent. Subject knowledge is stored separately in reusable Knowledge Packs, allowing new disciplines to be added without changing the application code.
Status: Sprint 1 Complete · Version
v0.2.0· First Pack: Artificial Intelligence
- Domain-independent learning platform
- Powered by reusable Knowledge Packs
- Interactive visual knowledge maps
- Full-text search across topics
- Breadcrumb navigation for guided exploration
- Light and dark themes
- Responsive design for desktop and mobile
- Built with HTML, CSS, JavaScript, and Plotly
- AI Knowledge Pack available today
- Designed to support future packs including PMP, Cloud Computing, Python, Networking, DevOps, Data Science, Cyber Security, and Enterprise Automation
Traditional learning often presents information as isolated pages, documents, videos, or courses.
Knowledge Explorer takes a different approach.
Instead of navigating content linearly, learners explore subjects visually, understanding how concepts connect and where individual topics fit within the broader knowledge landscape.
The goal is to make learning:
- Visual
- Structured
- Contextual
- Discoverable
- Scalable
Knowledge Explorer
https://rishibharaj.github.io/Learning/
Explore the current AI Knowledge Pack through an interactive knowledge map.
Knowledge Explorer separates the learning engine from the domain knowledge.
Knowledge Explorer Engine
+
Knowledge Pack
=
Learning Experience
The engine knows nothing about Artificial Intelligence, PMP, Cloud Computing, or any other domain.
Each subject is supplied through a structured Knowledge Pack.
This architecture enables new domains to be added without modifying the application itself.
Knowledge Pack
↓
Structured Topic Hierarchy
↓
Interactive Sunburst Map
↓
Topic Exploration
↓
Descriptions & Use Cases
↓
Knowledge Discovery
Users can:
- Explore a subject visually
- Drill into specific topics
- Understand parent-child relationships
- Search for concepts
- Follow guided navigation paths
- Learn how topics relate to the wider field
- Professional top navigation bar
- Explore, About, and Roadmap sections
- Responsive navigation experience
- Full-text topic search
- Instant topic discovery
- Click-to-navigate search results
- Plotly-powered sunburst visualization
- Full hierarchy exploration
- Root and child node navigation
- Dynamic drill-down experience
Each selected topic displays:
- Domain classification
- Plain-language description
- Practical use case
- Context within the wider subject
- Live navigation trail
- Click-to-jump hierarchy navigation
- Continuous location awareness
- Light theme
- Dark theme
- Responsive mobile support
- Automatic chart label scaling
| Knowledge Pack | Status |
|---|---|
| Artificial Intelligence | Live |
| PMP | Planned |
| Cloud Computing | Backlog |
| Python | Backlog |
| Networking | Backlog |
| DevOps | Backlog |
| Data Science | Backlog |
| Cyber Security | Backlog |
| Enterprise Automation | Backlog |
Most learning platforms tightly couple content and application logic.
Knowledge Explorer separates them.
Application Logic
+
Knowledge Pack Data
This approach enables:
- Domain independence
- Easier content expansion
- Consistent learning experience
- Long-term scalability
- Reusable learning architecture
A new subject should only require a new Knowledge Pack, not a software redesign.
- HTML5
- Modern CSS
- Vanilla JavaScript (ES6+)
- Plotly.js Sunburst Charts
- Space Grotesk
- Inter
- JetBrains Mono
- No frameworks
- No build process
- No external dependencies beyond Plotly
- Open and understandable architecture
Open the application directly:
# macOS
open index.html
# Windows
start index.html
# Linux
xdg-open index.htmlOr serve using a lightweight local server:
python3 -m http.serverKnowledge-Explorer/
├── index.html
├── README.md
├── ROADMAP.md
├── CHANGELOG.md
├── docs/
│ ├── PROJECT_STATE.md
│ ├── SPRINT_LOG.md
│ ├── BACKLOG.md
│ ├── DECISIONS.md
│ ├── ARCHITECTURE.md
│ ├── AI_CONTEXT.md
│ ├── JOURNEY.md
│ └── VISION_HISTORY.md
└── LICENSE
The current Sprint 1 implementation intentionally uses a single-file architecture for simplicity.
Future sprints will separate functionality into dedicated:
css/
js/
assets/
packs/
directories while preserving behavior.
Planned future enhancements include:
- Modular file architecture
- External Knowledge Pack loading
- Enhanced navigation controls
- Improved search capabilities
- PMP
- Cloud Computing
- Python
- Networking
- DevOps
- Data Science
- Cyber Security
- Enterprise Automation
A reusable knowledge engine capable of supporting virtually any structured learning domain.
- ROADMAP.md – Future development plan
- CHANGELOG.md – Version history
- PROJECT_STATE.md – Current project status
- ARCHITECTURE.md – Technical architecture
- DECISIONS.md – Architecture decision records
- BACKLOG.md – Deferred features and ideas
- JOURNEY.md – Project narrative
- VISION_HISTORY.md – Evolution of the product vision
Information becomes significantly easier to understand when learners can see structure, relationships, and context.
Knowledge Explorer transforms subjects from collections of topics into navigable knowledge architectures.
Learning is not just about consuming information.
Learning is about understanding how information connects.
TBD
Rishi Bharaj
PMP® | Oracle Generative AI Professional | ISO 9001 Lead Auditor
Operations Transformation • Knowledge Management • Learning Systems • Process Improvement