KnowledgeForge: Market-Driven Visual Analytics

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JacobBillings
Project Owner

KnowledgeForge: Market-Driven Visual Analytics

Expert Rating

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Overview

I propose creating KnowledgeForge, a marketplace platform that democratizes access to knowledge graph analytics while ensuring high-quality outcomes through market-driven evaluation mechanisms. This platform will provide critical tooling for the OpenCog Hyperon ecosystem, with particular focus on enhancing AcropolisOS's autonomous community infrastructure through visual analytics capabilities.

RFP Guidelines

Advanced knowledge graph tooling for AGI systems

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $350,000 USD
  • Proposals 39
  • Awarded Projects 5
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SingularityNET
Apr. 16, 2025

This RFP seeks the development of advanced tools and techniques for interfacing with, refining, and evaluating knowledge graphs that support reasoning in AGI systems. Projects may target any part of the graph lifecycle — from extraction to refinement to benchmarking — and should optionally support symbolic reasoning within the OpenCog Hyperon framework, including compatibility with the MeTTa language and MORK knowledge graph. Bids are expected to range from $10,000 - $200,000.

Proposal Description

Our Team

Jacob Billings, Ph.D. (Computational Neuroscience): Project lead with 15+ years in complex systems
and network analytics. Specializing in knowledge representation systems and data visualization.


Fluran: AI & Social Scientist serving as Tech lead with expertise in LangChain, graphs, agents, vector
databases, GraphRAG, and knowledge bases (neo4j).

Victor: Project Management expert with Silicon Valley product development experience.

Company Name (if applicable)

Company Name (if applicable) (optional)

Project details

Solution Architecture:

1. MeTTa/MORK Integration Layer
- Native connectivity with MORK's high-performance graph database
- Bidirectional translation between visual operations and MeTTa expressions
- Optimized query handling for billion-scale knowledge graphs
- Real-time symbolic computation with MORK's zipper-based VM
2. Visual Analytics Core Engine
- Interactive knowledge graph visualization and exploration tools
- Pattern recognition algorithms for structural and semantic analysis
- Comparative visualization for before/after impact assessment
- Temporal tracking of knowledge graph evolution
3. Analytics Marketplace
- Decentralized registry of analytics services with quality metrics
- Market-driven evaluation system with reputation scores and usage statistics
- Compensation framework for analytics service creators
- Standardized interface for analytics service consumption
4. AI-Assisted Development Environment
- Tools for non-experts to create knowledge graph analytics
- Neural-symbolic integration with PyNeuraLogic for deep learning on graphs
- Component library for rapid analytics service composition
- Testing framework for quality assurance
5. AcropolisOS Integration Framework
- API layer specifically designed for autonomous communities
- Knowledge visualization tools for community self-governance
- Programmatic interfaces for agent-driven analytics
- Community knowledge evolution tracking

Market-Driven Quality Assurance:
KnowledgeForge introduces a paradigm shift in how analytics services are evaluated and improved. Rather than relying solely on traditional metrics or expert review, the platform employs market mechanisms to determine the value and quality of analytics routines: 

- Multi-dimensional Quality Metrics: Each analytics service is evaluated across multiple dimensions
including accuracy, performance, interpretability, and utility.Usage-Based 
- Reputation: Services gain reputation based on actual usage patterns, with successful applications in real-world scenarios increasing a service's standing.
- Community Feedback Loops: Users provide feedback that directly influences service rankings and visibility within the marketplace.
- Value-Based Compensation: Analytics service creators are compensated based on the demonstrated value of their contributions, creating incentives for high-quality, useful services.
- Evolutionary Improvement: The marketplace encourages iterative refinement as analytics services compete for usage, with successful approaches inspiring new innovations.

This approach ensures that even AI-generated analytics services are subject to rigorous, practical
evaluation based on their actual utility in solving real-world knowledge graph challenges.

AcropolisOS Integration:
The proposed platform is specifically designed to enhance AcropolisOS's autonomous community infrastructure by providing:
1. Community Knowledge Visualization: Interactive tools for visualizing the collective knowledge of autonomous communities, making implicit structures explicit and navigable.
2. Decision Support Analytics: Specialized services for analyzing governance processes, identifying consensus patterns, and highlighting areas requiring attention.
3. Resource Allocation Insights: Visual analytics for understanding resource distribution, utilization patterns, and optimization opportunities within communities.
4. Temporal Community Evolution: Tools for tracking how community knowledge, priorities, and structures evolve over time, providing insights into development trajectories.
5. Agent-Human Knowledge Bridge: Analytics services that translate between agent-readable knowledge structures and human-understandable visualizations, facilitating collaboration between autonomous agents and human community members.

These capabilities directly support AcropolisOS's vision of autonomous communities by providing the visual interfaces and analytics tools needed for effective self-governance and knowledge management.

Technical Approach:
The platform will be developed using a modular, open-source architecture that prioritizes:
1. Interoperability: All components will expose standardized APIs and support common knowledge graph formats, with special attention to MeTTa expressions and MORK database compatibility.
2. Scalability: The system will leverage MORK's performance characteristics to handle knowledge graphs of varying sizes, from small community graphs to billion-node structures.
3. Extensibility: The architecture will support plugin-based extension, allowing new analytics services, visualization types, and integration points to be added without modifying the core platform.
4. Accessibility: The user experience will be designed to accommodate users with varying levels of technical expertise, from knowledge graph novices to expert analysts.
5. Security: The platform will implement comprehensive access controls and data protection measures to ensure sensitive knowledge graphs remain secure.


The development process will follow an iterative approach, with early releases focusing on core functionality and AcropolisOS integration, followed by marketplace capabilities and advanced analytics services.

Expected Impact:
Upon successful implementation, KnowledgeForge will provide several key benefits to the SingularityNET ecosystem:
1. Democratized Access: Making knowledge graph analytics accessible to a wider audience, reducing the expertise barrier.
2. Quality Improvement: Establishing market mechanisms that drive continuous improvement in analytics quality and relevance.
3. Community Empowerment: Providing autonomous communities with the tools needed to understand and leverage their collective knowledge.
4. Innovation Acceleration: Creating incentives for the development of novel analytics approaches through the marketplace model.
5. Cross-Domain Application: Supporting knowledge graph applications across multiple domains, from scientific research to governance to education.

By combining technical innovation with market-driven quality assurance and direct AcropolisOS
integration, KnowledgeForge represents a comprehensive solution to the challenges of knowledge graph
analytics within the SingularityNET ecosystem.

Open Source Licensing

MIT - Massachusetts Institute of Technology License

Background & Experience

Our team brings significant expertise in developing systems for knowledge representation and analysis:


Jacob Billings (Principal Investigator): Ph.D. in Computational Neuroscience (Emory University) focused on developing novel methods for analyzing brain network dynamics, providing fundamental insights into how complex systems organize and process information. As a researcher at the Czech Academy ofSciences and ISI Global Science Foundation, Jacob specialized in advanced statistical models for analyzing complex systems and developed innovative machine learning approaches for network analysis.

Fluran: Combines expertise in AI systems and social science, with practical experience implementing knowledge infrastructure using modern tools including LangChain, Neo4j graph databases, andGraphRAG systems.


Victor: Brings Silicon Valley product development expertise that ensures our technical solutions remain
grounded in user needs and practical implementation considerations.

Links and references

Research Profile: https://scholar.google.com/citations?user=Lmi2sg8AAAAJ/

Professional Background: https://www.linkedin.com/in/jacob-billings/

ORCID: https://orcid.org/0000-0002-8186-6126

Recent Publication: "Nature Prefers Sustainable Structures: Implications for Large-Scale Political
Self-Organization." Proceedings of 4th Virtual International Conference: Path to a KnowledgeSociety-Managing Risks and Innovation (PaKSoM).

Additional videos

 Additional videos (optional) (500 chars)

Describe the particulars.

Fluran and Victor tapped Jacob to work on the knowledge graph portion of their project. And we, together, formed this new team

Proposal Video

Not Avaliable Yet

Check back later during the Feedback & Selection period for the RFP that is proposal is applied to.

  • Total Milestones

    3

  • Total Budget

    $10,000 USD

  • Last Updated

    28 May 2025

Milestone 1 - MeTTa/MORK Integration & Visualization Foundation

Description

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.

Deliverables

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

Budget

$4,000 USD

Success Criterion

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

Milestone 2 - Visual Analytics Engine & AcropolisOS Integration

Description

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.

Deliverables

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

Budget

$4,000 USD

Success Criterion

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

Milestone 3 - Market-Driven Analytics Marketplace

Description

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.

Deliverables

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

Budget

$2,000 USD

Success Criterion

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

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