Luke Mahoney (MLabs)

Project Owner

Autograph: Factoring Knowledge Graphs into Frames

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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

There are many large, generic semantic graphs (e.g. WikiData, DBpedia, etc.) alongside growing numbers of domain-specific ones. LLMs offer a quick path to KG extraction but introduce: Inconsistency: erratic, non-deterministic entity resolution; Inaccuracy: missing or hallucinated predicates; Context loss: unstable frames of reference; Confidence: displaying confidence regardless of accuracy; Speed: impractical at scale. We are developing an AGI-focused KG tooling suite tackling these challenges via four modules—Context Identification, Entity Management, Predicate Management, and Confidence Management. This proposal addresses Context Identification for efficient framing of noisy KGs.

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 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
$60,000_USD
Last Updated
27 May 2025

Milestone 1 - Block Factorization of WikiData Knowledge Graphs

Description

In this milestone we will ingest a JSON dump of WikiData selecting a suitable subset of the properties available and use it to create a test knowledge graph. We will write a production-quality implementation of the block factorization algorithm and test it on the resulting WikiData knowledge graph. We will detail the accuracy and computational efficiency of the decomposition and provide instructions on how to use the software for factorizing large graphs or as a recommender system.

Deliverables

Software for efficient block factorization of large knowledge graphs tooling for ingestion of WikiData and details of the results of factorizing the WikiData knowledge graph.

Budget

$30,000 USD

Success Criterion

Fully operational, general-purpose graph factorization software, with a demonstration on WikiData, and accompanying documentation.

Milestone 2 - Assign Entities & Predicates to Reference Frames

Description

This milestone will probabilistically assign entities and predicates associated with those entities to the frames of reference determined in the first milestone. A specific entity can be assigned to more than one frame of reference - the degree of belonging is constrained to be 1 or less but there is no constraint on the sum of frame of reference assignment values as appropriate. Assignment of entities between more than one frame of reference provides the link between the different elements of the KG. Assignment of entities will be demonstrated on the same WikiData KG used earlier.

Deliverables

Software for entity assignment of factorized knowledge graphs tooling for ingestion of factorized graphs and details of the results of assignment for the WikiData knowledge graph.

Budget

$20,000 USD

Success Criterion

Fully operational, entity assignment software with a demonstration on WikiData and accompanying documentation.

Milestone 3 - Frame of Reference Evaluation and Final Report

Description

The use of factorization into context-driven frames of reference enables more accurately guided and more efficient traversal of large KGs for specific tasks. This we believe introduces a focus mechanism which removes the superfluous information contained in large KGs that will be needed when using AGI-scale KGs for real-world tasks. This milestone will evaluate the quality of this focus.

Deliverables

Detailed report of experimental findings on the factorization and entity assignment of large KGs with efficiency and accuracy assessments determined using WikiData and other KGs as appropriate.

Budget

$10,000 USD

Success Criterion

Delivery of final report.

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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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Review Headline

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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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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.

Luke Mahoney (MLabs)

Project Owner

Luke will act as the Grants Manager on behalf of the MLabs AI team. He manages several similar grant projects at MLabs, including numerous Project Catalyst works.

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Chief Scientist

Co-founder and Chief Scientist, Dr Bedworth leads the MLabs AI team in developing radically new approaches to Deep Learning, self-directed knowledge acquisition, and AGI. He'll act as project lead.

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AI Engineer

Ibrahim Abdelghany has worked at MLabs AI on various novel approaches to deep learning and is currently leading the LLM workflow. He will lead the design and implementation strategy for the project.

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AI Engineer

Dr Banashree Sarma has built context embedding models, and LLMs for a number of languages and is a member of the MLabs NLP team. She will lead the tooling production and evaluation elements.

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