Atlas Knowledge Base

A decentralized knowledge base graph network for collaborative information sharing and discovery.

Links

A decentralized knowledge base graph network for collaborative information sharing and discovery.

Overview

Atlas Network is a blockchain-based knowledge platform that creates interconnected knowledge graphs, enabling collaborative information curation, semantic search, and decentralized knowledge validation.

To Do

A cartography of Wikipedia pages around data visualization
2d/3d node viewer in tkd (sigma.js/D3)

Key Features

Decentralized knowledge graph storage
Semantic linking and relationship mapping
Collaborative knowledge curation
Token-based content validation
Cross-referencing and citation tracking
AI-powered knowledge discovery

Technology Stack

Backend: Motoko on Internet Computer
Storage: Graph database on decentralized storage
Search: Semantic search algorithms
Frontend: SvelteKit with interactive graph visualization
AI: Integration with language models for content analysis

Development Status

Status: Concept phase

Atlas Knowledge Base - Decentralized Semantic Knowledge Engine

Project Status: Active Development
Technology Stack: SvelteKit, Internet Computer Protocol (ICP), CanDB, Motoko
Repository: /repos/atlas-knowledge-base
Template Base: Vite + SvelteKit + Motoko starter

Overview

Atlas Knowledge Base is a scalable semantic knowledge engine built on decentralized cloud architecture. It provides a sophisticated platform for organizing, visualizing, and exploring interconnected knowledge through graph-based structures, semantic tagging, and advanced file management capabilities.

Core Capabilities

Knowledge Organization

Semantic Tagging System: Hierarchical tag structures with parent-child relationships
Multi-format File Support: Markdown, JSON, ZIP archives, audio files, and general media
Metadata Extraction: Automatic frontmatter parsing from markdown files
Categorized Storage: Structured organization using vault-based partition keys

Graph Visualization

Interactive Knowledge Graphs: Dynamic visualization using Sigma.js and Graphology
Force-Directed Layouts: ForceAtlas2 algorithm for natural graph clustering
Node Relationships: Visual representation of file-tag connections and hierarchies
Search and Filtering: Real-time graph filtering by categories and search terms
Expandable Clusters: Interactive exploration of grouped knowledge nodes

File Processing Engine

Chunk-Based Storage: Large file handling through 2MB chunk splitting
Batch Upload Processing: ZIP file extraction with automatic metadata mapping
Markdown Processing: Frontmatter tag extraction and content rendering
Progress Tracking: Real-time upload progress with toast notifications
Duplicate Detection: SHA256-based file identification and deduplication

Technical Architecture

Backend Infrastructure

Multi-Canister Design: Separate services for Atlas, App, User, and Index management
CanDB Integration: Distributed database with automatic scaling
Motoko Smart Contracts: Type-safe backend with comprehensive error handling
Identity Management: Internet Identity integration for secure authentication

Data Model Structure

		vault#<atlas-slug>/
metadata                    # Atlas metadata and configuration
file#<filename>            # File metadata and content references
tag#<tag>#<file>          # Tag-to-file relationship mapping
chunk#<file>#<id>         # File content chunks for large files

Frontend Features

Responsive Design: Mobile-first interface with dark/light mode support
Command Menu: Keyboard-driven navigation and search (K support)
Data Tables: Sortable, filterable views with checkbox selection
Modal System: Drawer-based UI components for mobile optimization
Real-time Updates: Live progress tracking and status notifications

Advanced Functionality

Semantic Capabilities

Hierarchical Tagging: Support for nested tag structures (e.g., books/philosophy/ethics)
Cross-Reference Discovery: Automatic relationship detection between related content
Metadata Enrichment: Frontmatter parsing with custom attribute support
Content Indexing: Full-text search across file contents and metadata

Batch Operations

ZIP Archive Processing: Automatic extraction and processing of compressed knowledge bases
Metadata Inheritance: JSON sidecar files for enhanced file metadata
Bulk Tag Management: Array-based tag imports and updates
Content Validation: File type checking and format validation

Graph Analytics

Node Clustering: Automatic grouping of related knowledge components
Edge Weight Calculation: Relationship strength visualization
Path Discovery: Finding connections between disparate knowledge nodes
Centrality Analysis: Identifying key knowledge hubs and connectors

File Format Support

Primary Formats

Markdown (.md): Full frontmatter support with tag extraction
JSON (.json): Tag arrays and metadata objects
ZIP Archives: Batch processing with metadata mapping
Audio Files: Media metadata extraction and storage

Processing Pipeline

Upload Detection: File type identification and routing
Metadata Extraction: Frontmatter/JSON parsing for tags and attributes
Content Chunking: Large file splitting for blockchain storage
Relationship Mapping: Tag-to-file association creation
Graph Integration: Node and edge generation for visualization

Knowledge Graph Features

Visualization Engine

Sigma.js Integration: High-performance graph rendering
Custom Layouts: ForceAtlas2 physics simulation for natural clustering
Interactive Navigation: Pan, zoom, and click-to-explore functionality
Category Filtering: Dynamic node visibility based on type classification

Node Types

File Nodes: Represent uploaded content with metadata
Tag Nodes: Hierarchical keyword categorization
Cluster Nodes: Aggregated groups of related content
Relationship Edges: Weighted connections between entities

Development Features

Testing Infrastructure

Playwright E2E Tests: Comprehensive browser automation testing
Motoko Unit Tests: Backend logic validation using Mops
PicJS Integration: Canister testing framework for IC development
Continuous Integration: Automated test pipelines

Developer Experience

Live Reload: mo-dev integration for backend development
Type Safety: Full TypeScript coverage with generated Candid bindings
Code Formatting: Prettier with Motoko and Svelte plugins
Component Library: Reusable UI components with shadcn/ui styling

Scalability Architecture

Storage Scaling

Partition-Based Design: Automatic canister scaling as data grows
Chunk Distribution: Load balancing across multiple storage canisters
Index Optimization: Efficient querying through structured sort keys
Cache Management: Strategic caching for frequently accessed content

Performance Optimization

Lazy Loading: On-demand content fetching for large datasets
Progress Streaming: Real-time feedback during bulk operations
Parallel Processing: Concurrent file uploads and processing
Memory Efficiency: Streaming operations for large file handling

Atlas Knowledge Base represents a sophisticated approach to decentralized knowledge management, combining semantic organization with powerful visualization tools and robust file processing capabilities, all built on censorship-resistant blockchain infrastructure.

Atlas Knowledge Base - Decentralized Semantic Knowledge Engine

Overview

Core Capabilities

Knowledge Organization

Semantic Tagging System: Hierarchical tag structures with parent-child relationships
Multi-format File Support: Markdown, JSON, ZIP archives, audio files, and general media
Metadata Extraction: Automatic frontmatter parsing from markdown files
Categorized Storage: Structured organization using vault-based partition keys

Graph Visualization

Interactive Knowledge Graphs: Dynamic visualization using Sigma.js and Graphology
Force-Directed Layouts: ForceAtlas2 algorithm for natural graph clustering
Node Relationships: Visual representation of file-tag connections and hierarchies
Search and Filtering: Real-time graph filtering by categories and search terms
Expandable Clusters: Interactive exploration of grouped knowledge nodes

File Processing Engine

Chunk-Based Storage: Large file handling through 2MB chunk splitting
Batch Upload Processing: ZIP file extraction with automatic metadata mapping
Markdown Processing: Frontmatter tag extraction and content rendering
Progress Tracking: Real-time upload progress with toast notifications
Duplicate Detection: SHA256-based file identification and deduplication

Technical Architecture

Backend Infrastructure

Multi-Canister Design: Separate services for Atlas, App, User, and Index management
CanDB Integration: Distributed database with automatic scaling
Motoko Smart Contracts: Type-safe backend with comprehensive error handling
Identity Management: Internet Identity integration for secure authentication

Data Model Structure

		vault#<atlas-slug>/
metadata                    # Atlas metadata and configuration
file#<filename>            # File metadata and content references
tag#<tag>#<file>          # Tag-to-file relationship mapping
chunk#<file>#<id>         # File content chunks for large files

Frontend Features

Responsive Design: Mobile-first interface with dark/light mode support
Command Menu: Keyboard-driven navigation and search (K support)
Data Tables: Sortable, filterable views with checkbox selection
Modal System: Drawer-based UI components for mobile optimization
Real-time Updates: Live progress tracking and status notifications

Advanced Functionality

Semantic Capabilities

Hierarchical Tagging: Support for nested tag structures (e.g., books/philosophy/ethics)
Cross-Reference Discovery: Automatic relationship detection between related content
Metadata Enrichment: Frontmatter parsing with custom attribute support
Content Indexing: Full-text search across file contents and metadata

Batch Operations

ZIP Archive Processing: Automatic extraction and processing of compressed knowledge bases
Metadata Inheritance: JSON sidecar files for enhanced file metadata
Bulk Tag Management: Array-based tag imports and updates
Content Validation: File type checking and format validation

Graph Analytics

Node Clustering: Automatic grouping of related knowledge components
Edge Weight Calculation: Relationship strength visualization
Path Discovery: Finding connections between disparate knowledge nodes
Centrality Analysis: Identifying key knowledge hubs and connectors

File Format Support

Primary Formats

Markdown (.md): Full frontmatter support with tag extraction
JSON (.json): Tag arrays and metadata objects
ZIP Archives: Batch processing with metadata mapping
Audio Files: Media metadata extraction and storage

Processing Pipeline

Upload Detection: File type identification and routing
Metadata Extraction: Frontmatter/JSON parsing for tags and attributes
Content Chunking: Large file splitting for blockchain storage
Relationship Mapping: Tag-to-file association creation
Graph Integration: Node and edge generation for visualization