Ramin Barati

Project Owner

Knowledge Graph Suite for Reliable AI

Expert Rating

n/a

Type SingularityNET RFP
Funding Request n/a
RFP Guidelines Advanced knowledge graph tooling for AGI systems

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Overview

We propose the development of a domain-agnostic tooling suite, comprising KGTool and KG Studio, to automate the construction, processing, and interrogation of knowledge graphs (KGs) for scientific literature. By integrating large language models with graph-based reasoning, the system will support end-to-end workflows: from entity/relation extraction through graph refinement and fact-grounded generation to interactive visualization. This infrastructure will enable reliable, explainable AGI reasoning over diverse research corpora.

Project Tags:
Community and Collaboration

RFP Guidelines

Advanced knowledge graph tooling for AGI systems

Internal Proposal Review

Type SingularityNET RFP
Total RFP Funding $350,000 USD
Proposals 39
Awarded Projects n/a

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

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Total Milestones
3
Total Budget
$60,000_USD
Last Updated
27 May 2025

Milestone 1 - Research & Design (1 Month)

Description

Define requirements for both the application layer (KG Studio) and core library (KGTool). Research optimal features and methods to meet user needs and technical goals.

Deliverables

(1) Comprehensive list of key features and user stories (2) Selection of algorithms methods and techniques to implement (3) Curated set of real-world datasets and evaluation metrics (4) System architecture diagrams and design documentation (5) Detailed timeline for implementation tasks

Budget

$12,000 USD

Success Criterion

(1) Feature list and user stories approved by stakeholders (2) Algorithms and evaluation plan validated for feasibility (3) Architecture and timeline reviewed and signed off

Milestone 2 - MVP Development & Evaluation (2 Months)

Description

Develop a minimum viable product demonstrating core end-to-end scenarios implement evaluation tools and conduct initial benchmark tests.

Deliverables

(1) Finalized feature set and user stories (2) MVP including KGTool core functions API endpoints and basic KG Studio workflows (3) Evaluation toolkit for measuring KGTool performance (4) Interim report detailing benchmark results and insights

Budget

$24,000 USD

Success Criterion

(1) MVP passes predefined functional tests for each workflow (2) Evaluation toolkit generates usable performance metrics (3) Benchmark report highlights strengths and areas for improvement

Milestone 3 - Final Delivery & Documentation (2 Months)

Description

Complete all components package the framework for distribution produce full documentation and deploy a demonstration website.

Deliverables

(1) Final technical report with performance analysis and recommendations (2) Packaged releases of KGTool KG API and KG Studio (3) Comprehensive developer and end-user documentation (4) Roadmap outlining future research and development directions (5) Live demonstration website showcasing KG Studio

Budget

$24,000 USD

Success Criterion

(1) All code and documentation published in public repository (2) Demo website operational and accessible to reviewers (3) Stakeholder approval of final deliverables and project completion

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The weighted average of the 4 perspectives Overall

0.0
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

0.0
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

0.0

Review Headline

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding. Viability (secondary) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
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An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

Ramin Barati

Project Owner

Oversees system design and architecture, specifies algorithms and user stories, leads MeTTa/Hyperon integration, drives research direction, and manages stakeholder communication.

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Amirhossein

AI Engineer

Develops and implements graph algorithms, network‑science methods, and MeTTa/Hyperon tooling; leads core research efforts and ensures robust AI engineering practices.

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farhoud mojahedzadeh

Data Engineer

Builds and maintains end‑to‑end data pipelines, backend/API services, and deployment workflows; integrates MeTTa/Hyperon, handles DevOps, and supports AI engineering tasks.

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Mehdi Balouchi

System Architect

Designs intuitive user experiences and interfaces, defines user stories, implements frontend workflows, and contributes to system architecture and algorithmic features.

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