Udai Solanki

Project Owner

NeuroKG – A Scalable Knowledge Graph Toolkit

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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

NeuroKG seeks to create a modular and open-source toolkit that will improve every stage of the lifecycle of knowledge graphs (KGs) in relation to AGI systems. The toolkit will center on the following: Extraction>> pertains to the automated creation of KGs from unstructured data. Refinement>>focuses on duplication, disambiguation, and consistency checking. Assessment>> relates to benchmarking the quality of KGs and their reasoning abilities. Integration>> MORK graph database within the OpenCog Hyperon framework from MeTTa language offers complete proprietary integration. NeuroKG will enable the construction of highly resilient, agile, and expandable KGs in AGI systems.

RFP Guidelines

Advanced knowledge graph tooling for AGI systems

Proposal Submission (7 days left)

Type SingularityNET RFP
Total RFP Funding $350,000 USD
Proposals 8
Awarded Projects n/a

SingularityNET

Apr. 16, 2025

SingularityNET

rfp owner

Advanced knowledge graph tooling for AGI systems

Proposal Submission (7 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
4
Total Budget
$150,000_USD
Last Updated
11 May 2025

Milestone 1 - Setup Infrastructure & Initiating Basic Framework

Description

Execute the initial infrastructure consisting of Kubernetes Kafka and back-end storage systems. Set up the project skeleton along with CI/CD pipelines along with the first microservice interaction templates.

Deliverables

Active Kubernetes cluster and AWS login; Apache Kafka topics created; S3-compatible storage provisioned; primary Git repository and CI/CD jobs configured.

Budget

$20,000 USD

Success Criterion

All core components are installed and working; data flow in sample form is functioning between services; primary logs, data, and metrics are collected.

Milestone 2 - Extracting the data and Drafting The Initial Graph

Description

Create and deploy the document text extraction process using specialized NER and RE models. Connect the NLP engine to a JanusGraph database to automatically construct knowledge graphs.

Deliverables

Functional Transformer models; PDF/HTML content intake at the pipeline; initial graph building and exporting procedure; processed a batch of 10000 documents.

Budget

$50,000 USD

Success Criterion

Complete NER/RE with precision of at least 80% on the holdout set; RDF triples were derived from authentic documents; graph accessible through the REST API.

Milestone 3 - Refinement Graph and Human-in-the-Loop Interface

Description

Create UI for interactive quality control for graph editing and deploy deduplication entity linking consistency checking and rule-based graph quality control.

Deliverables

Entity resolution processes; datalog/prolog information processing rule construction engine; React-derived KG curation dashboard; change history tracking of KG modifications.

Budget

$39,987 USD

Success Criterion

Resolve minimum 90% duplicates in pilot data; logic constraints enforced; active user testing for UI pertaining to entity disambiguation and feedback mechanisms.

Milestone 4 - Evaluation Suite & AGI Integration Tools

Description

Design evaluation metrics for Knowledge Graph (KG) quality and readiness for AGI. Complete the integrations of MeTTa and MORK. Make the SDK and CLI available to developers.

Deliverables

Documented metrics benchmark datasets MeTTa script generator MORK connector SDK and CLI.

Budget

$40,013 USD

Success Criterion

All active reasoning benchmarks meet operational status; Hyperon accesses NeuroKG through MORK; external collaborators evaluate developer’s toolkit.

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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

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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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Udai Solanki

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Aditya

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