This milestone establishes the core technical infrastructure for KnowledgeForge focusing on deep integration with the MeTTa language and MORK database while building the foundation for visual analytics. Key activities include: 1. Developing a robust MORK connection layer with optimized read/write operations 2. Creating a MeTTa expression parser and evaluator for translating between visual operations and symbolic expressions 3. Implementing foundational data structures for knowledge graph representation 4. Building a prototype visualization engine to demonstrate successful integration 5. Establishing performance benchmarks for core operations 6. Creating comprehensive documentation of integration points and APIs 7. Setting up continuous integration and testing infrastructure This milestone creates the essential technical foundation upon which all subsequent development will build ensuring seamless integration with the OpenCog Hyperon ecosystem.

1. MORK database integration module with comprehensive API documentation 2. MeTTa expression handler with support for pattern matching and equality inference 3. Knowledge graph data structures optimized for visualization operations 4. Prototype visualization engine demonstrating successful MORK/MeTTa integration 5. Comprehensive technical documentation including: System architecture diagrams API specifications Integration patterns Development guide 6. Automated test suite with 80%+ coverage 7. Performance benchmark report for basic operations 8. GitHub repository with all source code and documentation

$4,000 USD

The milestone succeeds when the system can: 1. Load knowledge graphs of 100,000+ nodes from MORK with query response times under 500ms 2. Parse and evaluate MeTTa expressions for basic graph operations 3. Translate visual operations into equivalent MeTTa expressions and back 4. Render interactive visualizations of knowledge graphs retrieved from MORK 5. Pass all automated tests with 80%+ code coverage 6. Demonstrate the end-to-end flow from MORK database to visual representation in a working prototype

This milestone focuses on building a comprehensive visual analytics engine and establishing integration with AcropolisOS to support autonomous communities. Key activities include: 1. Developing advanced visualization capabilities for knowledge graphs 2. Creating interactive exploration and filtering tools 3. Implementing pattern detection algorithms for structural and semantic analysis 4. Building specific visualization components for AcropolisOS community structures 5. Developing APIs for AcropolisOS integration 6. Creating demonstration applications for community knowledge visualization 7. Optimizing performance for interactive usage This milestone delivers the core visual analytics capabilities of the platform while establishing direct utility for AcropolisOS communities.

1. Visual analytics engine with multiple visualization types: Network/graph visualizations Hierarchical views Matrix representationsTemporal evolution displays 2. Interactive exploration tools: Dynamic filtering Path tracing Node/edge inspection Semantic zooming 3. Pattern detection algorithms: Structural pattern identification Semantic relationship analysis Anomaly detection Community structure analysis 4. AcropolisOS integration components: API layer for AcropolisOS connectivity Specialized visualizations for community structures Resource allocation analytics Governance pattern visualization 5. Demonstration applications: Community knowledge explorer Governance analytics dashboard Resource allocation analyzer 6. Comprehensive documentation and tutorials 7. Performance optimization report

$4,000 USD

The milestone succeeds when: 1. The visual analytics engine can render and enable interactive exploration of knowledge graphs with 500,000+ nodes 2. Pattern detection algorithms successfully identify structural and semantic patterns in test knowledge graphs3. AcropolisOS integration components successfully connect to and visualize autonomous community structures 4. Demonstration applications show practical utility for community governance and resource allocation 5. Performance benchmarks show interactive response times (<250ms) for typical user operations 6. User testing confirms intuitive exploration and insight generation from complex knowledge structures

This milestone creates the marketplace infrastructure that enables the sharing evaluation and improvement of knowledge graph analytics services through market mechanisms. Key activities include: 1. Developing the marketplace architecture with service registration and discovery 2. Creating standardized interfaces for analytics services 3. Implementing multi-dimensional quality and reputation metrics 4. Building the compensation and incentive system 5. Developing user feedback and rating mechanisms 6. Creating service cataloging and categorization systems 7. Implementing marketplace analytics and trending metrics This milestone establishes the infrastructure for continuous improvement of analytics quality through market-driven evaluation and incentives.

1. Marketplace architecture: Service registry and discovery system Standardized service interface specification Service versioning and lifecycle management 2. Quality assurance framework: Multi-dimensional quality metrics Usage-based reputation system Feedback collection and aggregation Comparative benchmarking tools 3. Compensation system:Usage tracking Value attribution Revenue distribution mechanisms 4. User interface components: Service discovery and browsing Detailed service information and metrics Comparison and selection tools User reviews and ratings 5. Analytics services catalog: Categorization system Tagging and metadata framework Search and filtering capabilities 6. Marketplace analytics: Usage trends Quality evolution metrics Creator performance analytics 7. Comprehensive documentation and user guides

$2,000 USD

The milestone succeeds when: 1. Analytics services can be registered, discovered, and used through the marketplace 2. Quality metrics accurately reflect service performance across multiple dimensions 3. The reputation system responds appropriately to usage patterns and user feedback 4. The compensation system correctly tracks usage and allocates revenue to service creators 5. Users can effectively browse, compare, and select services based on their needs 6. Marketplace analytics provide meaningful insights into service quality and usage trends 7. At least three example analytics services demonstrate the full lifecycle from creation to evaluation