Innovations Labs

Project Owner

Symbolic Graph Query Suite for OpenCog Hyperon

Expert Rating

n/a

Type SingularityNET RFP
Funding Request $75,000 USD
RFP Guidelines Advanced knowledge graph tooling for AGI systems

Overview

A modular, open-source symbolic graph query engine and benchmarking framework for OpenCog Hyperon. This project aims to validate, profile, and optimize symbolic reasoning performance using MeTTa, Atomspace, and MORK. It will provide tools for AGI researchers to query knowledge graphs, run benchmarks, and visualize inference metrics.

Project Tags:
Community and Collaboration

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

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

Our team unites academic depth with AI development expertise. Dr. Joseph Iyanda offers scientific research. Victor Asuquo leads in AI/ML, specializing in NLP and vision. Obongofon Udombat excels in data science and GenAI. Godspower Ufot is our frontend web specialist. Ubongabasi Jerome provides backend engineering. Daniel Udo adds NLP proficiency for personalized feedback systems. This blend of skills ensures robust AGI motivation framework development.

Company Name (if applicable)

Innovations Labs

Project details

We propose to develop a performant, extensible Symbolic Graph Query and Benchmark Suite to support reproducible research and tooling for symbolic inference validation within OpenCog Hyperon. The system will encompass:

- A query engine for executing symbolic graph queries using MeTTa and interfacing with Atomspace.
- A benchmark framework to evaluate correctness, performance, and scalability of reasoning processes.
- An optional visual dashboard for live inspection of queries and graph reasoning behavior.

This suite will directly support Hyperon development by enabling faster iteration, deeper insights into inference strategies, and cross-comparisons of cognitive procedures. It will also serve as a valuable community asset for debugging, optimization, and publishing reproducible experiments.

The project will be fully open-source and developed over 6 months, incorporating community feedback cycles and comprehensive documentation. Deliverables include source code, test graph libraries, benchmark cases, performance reports, and integration examples.

Open Source Licensing

MIT - Massachusetts Institute of Technology License

Background & Experience

AI-Powered Education Research: Investigation into Generative AI's role in high school assessments, enhancing self-assessment, metacognition, and feedback mechanisms across learning paradigms.

AI Lab: Development of a platform with AI tools for various applications (ideation, image/video generation, programming, data analysis, education, and task automation), coupled with a prompt engineering resource and educational materials.

GenAI Cognitive Impact Research: Examination of the ethical use of AI by students and professionals, with a focus on mitigating negative effects and promoting productivity.

African Language AI Development: Development of AI models for African languages, including Pidgin sentiment analysis and speech-to-text error reduction.

Enterprise-Level Microservice Applications: Design and development of enterprise-level microservice applications.

Model Performance Optimization: Optimization of model performance, resulting in a significant improvement in recommendation accuracy.

Links and references

https://github.com/ubyjerome

https://github.com/opencog/hyperon-experimental

https://wiki.opencog.org/w/MeTTa

https://github.com/Victorasuquo

https://www.linkedin.com/in/obongofon-udombat-a30414146/ https://github.com/ubyjerome

https://www.myeasyschool.com.ng/

Activity Summary

Milestones

3

Total

Discussion

0

Total Comments

Reviews

0

Total Posted

Project Team

7

Total People

Total Milestones
3
Total Budget
$75,000_USD
Last Updated
20 May 2025

Milestone 1 - Research Planning and Design Specification

Description

Timeline: Month 1

Deliverables

Deliverable Description: A comprehensive research and implementation plan for CIAI, defining scope, objectives, and methodologies. Agile work breakdown structure detailing phases, timelines, dependencies, and responsible roles. Conceptual and technical design of the CIAI architecture, including: Information-theoretic conceptual blending engine Multi-agent system role mapping FFCA-based knowledge representation in Atomspace Technical risk analysis and mitigation strategy

Budget

$15,000 USD

Success Criterion

Success Criteria: Submission of a detailed, peer-review-ready research plan Completion and approval of the framework/system design document Agile project plan with clear sprint timelines and deliverables Positive feedback from at least one OpenCog Hyperon core contributor or reviewer on feasibility and alignment

Milestone 2 - Prototype Development and Benchmark Testing

Description

Timeline: Month 2 to Month 4

Deliverables

Deliverable Description: Implementation of a functional prototype of CIAI demonstrating: MeTTa-based blending agent using entropy/mutual information Fuzzy Formal Concept Analysis (FFCA) engine integrated with Atomspace Working multi-agent collaboration: Retriever, Blender, Evaluator, Checker Initial integration tests with Atomspace and MeTTa expressions Benchmark suite for evaluating: Novelty and coherence of generated blends Query performance and system responsiveness Documentation of architecture, agent interactions, and early insights

Budget

$30,000 USD

Success Criterion

Success Criteria: All core agents functional and able to process a complete prompt-to-concept generation cycle Demonstrable improvement over baseline symbolic blending methods (quantified with semantic similarity and novelty metrics) Successful execution of benchmark tests on at least 10 concept blending cases Intermediate report submitted, including source code, benchmark results, and testing logs

Milestone 3 - Final Integration, Evaluation & Public Release

Description

Timeline: Month 5 to Month 6

Deliverables

Deliverable Description: Fully integrated CIAI framework: Robust MeTTa modules for blending, concept evaluation, and workflow management Distributed system capable of handling parallel blending evaluations Comprehensive evaluation report including: LLM-based and human evaluation metrics on creativity and coherence Stress-testing under variable data volumes and noise conditions Public GitHub repository with: MIT-licensed open-source code Full documentation (developer guides, API references, use cases) Reproducible examples and preloaded test datasets Video demonstration and slide deck for community presentation

Budget

$30,000 USD

Success Criterion

Success Criteria: Submission of a complete, documented framework with working reproducibility on external systems All major project goals met as per proposal Community demo successfully executed (live or recorded) Approval from project stakeholders and/or acceptance into OpenCog Hyperon toolchain repository

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The weighted average of the 4 perspectives Overall

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Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

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This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

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