Completed

ivan reznikov

Project Owner

MeTTa-Driven KG Service with LLM Integration

Project for Round 4 - Beta
Funding Awarded $50,000 USD
Funding Pools SingularityNET RFPs
Milestones 4 / 4 Completed

Status

Overall Status
🥳 Completed & Paid
Funding Transfered
$50,000_USD
Max Funding Amount
$50,000_USD

Funding Schedule

View Milestones

Milestone Release 1	$5,000 USD	Transfer Complete	14 Nov 2024
Milestone Release 2	$15,000 USD	Transfer Complete	19 Jul 2024
Milestone Release 3	$20,000 USD	Transfer Complete	19 Jul 2024
Milestone Release 4	$10,000 USD	Transfer Complete	10 Oct 2024

Overview

This proposal is structured to align with the requirements of the RFP for the MeTTa-Driven Knowledge Graph Service with LLM Integration. Our goal is to develop a service on the SingularityNET platform, integrating a custom knowledge graph builder with LLM capabilities. This service will allow natural language querying and intelligent insights extraction through Neo4j, making data analysis more accessible and user-friendly.

Proposal Description

How Our Project Will Contribute To The Growth Of The Decentralized AI Platform

Our project will significantly enhance the AI platform's capabilities by introducing a sophisticated Knowledge Graph Service integrated with Large Language Models (LLM). This service will enable more intuitive and effective data interaction, promoting wider accessibility and usability of AI technologies. By allowing users to query complex data using natural language, we're expanding the platform's functional range and making advanced data analysis more accessible to a broader user base.

Neo4j for Knowledge Graph ManagementLLMs for Data PreprocessingSingularityNET's AI Services IntegrationMeTTa-Motto Library

The core problem we are aiming to solve

The primary problem our project addresses is the complexity and inaccessibility of sophisticated data analysis for users who need technical expertise. Interacting with complex data sets requires specialized knowledge, limiting the user base. Our service will democratize access to this data by providing an intuitive natural language interface.

Our specific solution to this problem

We understand the need for a service that not only integrates advanced knowledge graph technology with natural language processing but also aligns with the SingularityNET platform, specifically using MeTTa-Motto for data processing and query interpretation. Our focus is on creating a service that is intuitive for users, allowing them to interact with complex data in a straightforward manner.Our solution is a custom Knowledge Graph Service that leverages LLM for natural language processing, integrated with Neo4j. This service will preprocess data from the Standard Template Construct for ingestion into Neo4j, allowing users to build custom knowledge graphs. The integrated LLM will interpret natural language queries and convert them into queries for Neo4j, thus facilitating intelligent data exploration. This approach will make complex data analysis more accessible and intuitive.

Activity Summary

Milestones

4

Total

Discussion

0

Total Comments

Reviews

2

Total Posted

Project Team

1

Total People

Reviews & Rating

New reviews and ratings are disabled for Awarded Projects

Overall Community

3.5

from 2 reviews

5

0
4

1
3

1
2

0
1

0

Feasibility

5

from 2 reviews

Viability

4.5

from 2 reviews

Desirabilty

3.5

from 2 reviews

Usefulness

3

from 2 reviews

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2 ratings

Expert Review 1
Overall

3.0
- Feasibility 5.0
- Desirabilty 3.0
- Usefulness 2.0
Solid proposal, but for me no open source no go

The proposer is highly qualified and knows the environment and the tooling rather well. To folks deep within MeTTa and Open Cog it must all fit, but I have to go on what's in the actual proposal.

It would help my evaluation to hear more functional detail than an NLP front end to query Neo4j. A part of an ongoing development, but no hint where and how. And to reiterate, I have little interest in a technology grant that's not OSS.

Expert Review 2
Overall

4.0
- Feasibility 5.0
- Desirabilty 4.0
- Usefulness 4.0
Knowledge Graphs and LLMs, what\'s not to love.

pending...

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Overall

Confidence level that the project is possible at all Feasibility

0
Confidence in a successful outcome considering team, time, and budget Viability

0
Market fit - Balancing needs and benefits against competition Desirabilty

0
To what extent will the project help the AI platform grow? Usefulness

0

Review Headline

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Review Summary

0 /5000 chars

Expert Review

Overall

Confidence level that the project is possible at all Feasibility

0
Confidence in a successful outcome considering team, time, and budget Viability

0
Market fit - Balancing needs and benefits against competition Desirabilty

0
To what extent will the project help the AI platform grow? Usefulness

0

Review Headline

0 /50 chars

Review Summary

0 /5000 chars

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.

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.

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

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.

Total Milestones
4
Total Budget
$50,000_USD
Last Updated
24 Mar 2026

Milestone 1 - Knowledge Graph Creation

Status

😀 Completed

Description

Development of a MeTTa-Driven Knowledge Graph incorporating STC data.

Deliverables

A functional bespoke knowledge graph system.

Target Date

November 14, 2024

Budget

$5,000 USD

Link URL

Link 1 Link 2 Reviewer's report

Milestone 2 - Integration Of KGs & LLMs

Status

😀 Completed

Description

Integrating LLMs for preprocessing STC data and enabling natural language query capabilities.

Deliverables

An LLM-integrated system with natural language query functionality.

Target Date

July 19, 2024

Budget

$15,000 USD

Link URL

Link 1 Reviewer's report

Milestone 3 - Implementation Of SNET Services

Status

😀 Completed

Description

Developing a web interface for service access and integrating Neo4j for intelligent data querying.

Deliverables

A web-based frontend and a fully integrated Neo4j system.

Target Date

July 19, 2024

Budget

$20,000 USD

Link URL

Link 1 Reviewer's report

Milestone 4 - Payment Integration

Status

😀 Completed

Description

Finalizing the service with AGIX payment integration and thorough testing.

Deliverables

A complete operational service with AGIX payment system and robust testing.

Target Date

October 10, 2024

Budget

$10,000 USD

Link URL

Demo Code Link 3

Join the Discussion (0)

Expert Ratings

Reviews & Ratings

New reviews and ratings are disabled for Awarded Projects

Expert Review 1
Overall

3.0
- Feasibility 5.0
- Desirabilty 3.0
- Usefulness 2.0
Solid proposal, but for me no open source no go

The proposer is highly qualified and knows the environment and the tooling rather well. To folks deep within MeTTa and Open Cog it must all fit, but I have to go on what's in the actual proposal.

It would help my evaluation to hear more functional detail than an NLP front end to query Neo4j. A part of an ongoing development, but no hint where and how. And to reiterate, I have little interest in a technology grant that's not OSS.

Expert Review 2
Overall

4.0
- Feasibility 5.0
- Desirabilty 4.0
- Usefulness 4.0
Knowledge Graphs and LLMs, what\'s not to love.

pending...

The weighted average of the 4 perspectives Overall

0.0
To what extent will the project help the AI platform grow?Usefulness

0.0
Confidence level that the project is possible at allFeasibility

0.0
Market fit - Balancing needs and benefits against competitionDesirabilty

0.0

Review Headline

0 /50 chars

Review Summary

0 /5000 chars

The weighted average of the 4 perspectives Overall

0.0
To what extent will the project help the AI platform grow?Usefulness

0.0
Confidence level that the project is possible at allFeasibility

0.0
Market fit - Balancing needs and benefits against competitionDesirabilty

0.0

Review Headline

0 /50 chars

0 /5000 chars

Warning: Adding final group rating for this project will prevent expert users from adding new or editing existing reviews

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.

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

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.

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.

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

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.

ivan reznikov

Project Owner

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