Darshana Patel

Project Owner

IONATION® for Emergent Knowledge Graphs

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

This proposal introduces IONATION®—a vibrational intelligence framework—as a novel enhancement to the development and refinement of knowledge graphs for AGI systems. By mapping tension, resonance, and relational flow, IONATION® offers new methods for identifying dissonance, structuring semantic meaning, and dynamically guiding graph evolution. Applied to tools compatible with MeTTa and MORK, it supports symbolic reasoning, concept formation, and adaptive coherence across large-scale knowledge systems. This proposal bridges energetic perception with neuro-symbolic function to enable more emergent, meaningful concept graphs.

RFP Guidelines

Advanced knowledge graph tooling for AGI systems

Internal Proposal Review

Type SingularityNET RFP
Total RFP Funding $350,000 USD
Proposals 40
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
$200,000_USD
Last Updated
19 May 2025

Milestone 1 - Framework Design & Technical Roadmap

Description

Establish a clear technical architecture for how IONATION® will function as a symbolic resonance layer for emergent knowledge graph refinement. This phase includes formalizing a vibrational logic schema to evaluate coherence and contradiction within graph structures and define mappings between IONATION’s conceptual ontology and MeTTa expressions.

Deliverables

Deliverables: • Technical specification for “IONATION® for Emergent KGs” • Ontological mappings between IONATION® and MeTTa/MORK • Field-weighting logic for symbolic reasoning • Agile development roadmap and resource timeline • Preliminary MeTTa module scaffolding

Budget

$40,000 USD

Success Criterion

Success Criteria: • Signed-off system design by advisors/community reviewers • Schema compatibility validated with Hyperon documentation • MeTTa symbolic mappings confirmed via small-scale unit test • Clear pathway to graph refinement prototype development

Milestone 2 - Prototype Development & Testbed Evaluation

Description

Build a working prototype that implements the IONATION® framework’s logic for identifying and adapting dissonance in symbolic graph structures using MeTTa and MORK. This includes fuzzy/paraconsistent concept blending tension detection node weighting and field-based abstraction ranking. The tool will be tested using scientific knowledge graph datasets or comparable open-domain corpora.

Deliverables

Deliverables: • Functional prototype module for symbolic refinement • Integration with MORK-based graph testbed • Test case demonstrations using real-world data • Initial results: pattern detection anomaly identification and blending logic • Developer documentation and test instructions

Budget

$80,000 USD

Success Criterion

Success Criteria: • Working prototype with measurable concept generation and refinement • Ability to detect incoherence or contradiction within symbolic graphs • Support for fuzzy matching and resonance-weighted evaluation • Output usable by other MeTTa-compatible systems • Positive preliminary benchmarks on creativity, logic integrity, and graph compression

Milestone 3 - Deployment Optimization & Open Source Release

Description

Optimize the prototype for scale publish detailed documentation and release the tool as an open-source module for use by AGI developers. Add support for additional export formats (JSON GraphML MeTTa-compatible structures) and extend the resonance evaluation engine with API hooks for LLM-assisted creativity evaluation. Conduct workshops and publish use cases.

Deliverables

Deliverables: • Final codebase (MeTTa modules + tooling scripts) • Performance benchmark report on multi-hop reasoning and novelty detection • Visualization examples and demo walkthrough • Public GitHub repo with detailed instructions and modular interface • Case studies and whitepaper on IONATION® KG refinement logic

Budget

$80,000 USD

Success Criterion

Success Criteria: • Fully functioning open-source tool, validated on 10M+ edge graphs • Benchmarked against baseline KG refinement techniques • Adoption or testing interest from at least one AGI-aligned development team • Completion of documentation and onboarding materials • Positive reviewer and/or user feedback on novelty and symbolic clarity

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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.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary) Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference? Example:

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

Usefulness (secondary) This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?” For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?). For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things? Examples:

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
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.

Darshana Patel

Project Owner

Leads the conceptual design and application of the IONATION® framework within AGI knowledge graph systems. Translates vibrational intelligence theory into actionable symbolic schema.

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