Pragadeesh

Project Owner

Symbolic Graph Tooling for AGI Reasoning

Expert Rating

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

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Overview

This proposal aims to develop an open-source symbolic knowledge graph tooling system that enables AGI reasoning through dynamic data extraction, semantic refinement, anomaly detection, and MeTTa/MORK export. Designed for performance, explainability, and domain-agnostic use, this tool will help bridge raw data and symbolic cognition with real-time visualization and scalable knowledge representation. Built fully open and aligned with OpenCog Hyperon systems.

RFP Guidelines

Advanced knowledge graph tooling for AGI systems

Proposal Submission (5 days left)

Type SingularityNET RFP
Total RFP Funding $350,000 USD
Proposals 3
Awarded Projects n/a

SingularityNET

Apr. 16, 2025

SingularityNET

rfp owner

Advanced knowledge graph tooling for AGI systems

Proposal Submission (5 days left)

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.

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Proposal Description

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Total Milestones
6
Total Budget
$200,000_USD
Last Updated
7 May 2025

Milestone 1 - Graph Extraction & Preprocessing

Description

(Timeframe: Month 1) Set up data ingestion pipeline (text, JSON, publications). Use SpaCy, LangChain, and GROBID.

Deliverables

Working parser that converts raw inputs to entity triples.

Budget

$40,000 USD

Success Criterion

Successful parsing with >85% accuracy across varied formats.

Milestone 2 - Graph Construction & Storage

Description

(Timeframe: Month 2) Construct semantic graphs using RDFLib and store in Neo4j/GraphDB with query interface.

Deliverables

Queryable knowledge graph with RDF structure.

Budget

$30,000 USD

Success Criterion

Validated graph structure accessible via SPARQL or Cypher queries.

Milestone 3 - Refinement & Anomaly Detection

Description

(Timeframe: Month 3–4) Use PyKEEN and AmpliGraph to remove redundancies and detect contradictions. Integrate fact-checking modules.

Deliverables

Cleaned graph with semantic weighting and error markers.

Budget

$40,000 USD

Success Criterion

60% reduction in redundancy; error highlighting functioning on test graph sets.

Milestone 4 - Visualization & UI

Description

(Timeframe: Month 4) Develop browser-based visualizer using D3.js/Cytoscape.js.

Deliverables

Interactive graph viewer with node/edge editing and path highlighting.

Budget

$30,000 USD

Success Criterion

Graph navigation, updates, and tooltip overlays fully functional in test.

Milestone 5 - MeTTa Export & Evaluation

Description

(Timeframe: Month 5) Export knowledge graphs to MeTTa format and evaluate AGI tasks like QA and analogy resolution.

Deliverables

MeTTa-compatible exports + task evaluation reports.

Budget

$40,000 USD

Success Criterion

Tasks executable on symbolic agents with >75% test performance baseline.

Milestone 6 - Documentation & Release

Description

(Timeframe: Month 6) Create user/developer documentation, host on GitHub, and announce open-source launch.

Deliverables

Complete repo with docs, examples, and install guide.

Budget

$20,000 USD

Success Criterion

Public usage without issues, confirmed by community testing or initial forks.

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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.

Pragadeesh

Project Owner

Solo developer and researcher responsible for the complete design, development, testing, documentation, and release of the proposed knowledge graph tooling system.

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