The goal is to create a foundational KG that not only structures Deep Funding data for immediate needs but also sets the groundwork for scalable expansion to encompass the entire SingularityNET ecosystem.
Main purpose
The SingularityNET (SNET) ecosystem generates a large and diverse range of data, including technical documentation, educational materials, community interactions, proposals, votes and more. Currently, this wealth of information is scattered and siloed, limiting its usability. To address this, this RFP aims to fund the initial iteration of a Knowledge Graph (KG), focusing on Deep Funding-related data.The purpose of the KG is to structure the data with human-provided and automated MetaData, create and expose relations, and keep track of the data's trustworthiness. The KG will serve as the key database to enable multiple data Applications (e.g. UI, webapps, bots, etc.) that access the data in a structured way and present it in a context-sensitive manner for a variety of use cases.
Long description
Context and Background:
The SingularityNET (SNET) ecosystem generates a large and diverse range of data, including technical documentation, educational materials, community interactions, proposals, votes and more. Currently, this wealth of information is scattered and siloed, limiting its usability. To address this, this RFP aims to fund the initial iteration of a Knowledge Graph (KG), focusing on Deep Funding-related data.The purpose of the KG is to structure the data with human-provided and automated MetaData, create and expose relations, and keep track of the data's trustworthiness. The KG will serve as the key database to enable multiple data Applications (e.g. UI, webapps, bots, etc.) that access the data in a structured way and present it in a context-sensitive manner for a variety of use cases.
Collaboration:
This RFP will be followed by a subsequent RFP for applications that use the Knowledge Graph.Multiple applications (teams working on applications with frontend/user stories) will be funded through this future, separate RFP. The winning teams (Knowledge Graph team from this RFP and Application teams from the RFP for KG Applications) are expected to collaborate. The details are described below under non-functional requirements.Examples of suggested Applications for the Applications RFP:
Automated review of incoming proposals (on both drafts and published ones), with integration into SingularityNET voting portal.
Categorisation of proposals and filtering proposals (tagging can be predefined categories or bottom-up).
Proposal builder (AI helper, separate UI from voting platform): think about extra aspects and compare with other proposals.
General Q&A avatar on existing proposals and rules of DF round x.
RFP Expected Outcomes:
Initial Knowledge Graph Deployment: A functional KG that represents all Deep Funding content, structured for scalability and future integration across the ecosystem.
Data Ingestion: Ability to ingest data from the main portal via an API offered by DF (must have) as well as other sources (nice to have) and to store it in the KG.
Thorough Documentation: provide comprehensive documentation detailing the structure and functionality of the KG to allow other teams to work with and to work on the KG.
Collaboration with Application projects: Coordinate with and integrate support for the teams awarded in the Data Applications RFP. This includes making the necessary APIs available for connecting data pipelines to the KG and searching the data in the KG.
Maintenance & Hosting Plan: Accompany this with a maintenance plan to ensure ongoing support, hosting, and basic updates for 1 year (included in the maximum award).
Integration with SingularityNET's AI platform as a 'knowledge node': A minimum of 10% of the budget is suggested to be reserved for this purpose.
Functional Requirements
Description of the functions and features expected from the solution.Must Have:Initial KG functionality:
Search functionality: Ability for 3rd party applications to search the Knowledge Graph.
Data ingestion functionality: Ability for 3rd party applications to add data to the Knowledge Graph.
Data management functionality: Ability for admin to delete and/or edit data.
Tagging functionality: improve the organization of the content by ensuring that the different entities are appropriately tagged and clustered with rich metadata.
Good to Have:
Interface for edge list data import.
Automated categorization system of data objects.
Permission management for data ingestion, management, and search functionalities.
Nice to Have:Community Collaboration Features
Tools and interfaces that allow for community-driven data contributions, corrections, and annotations, fostering an engaged and participatory ecosystem.
A feedback loop or rating system that allows users to report inaccuracies or suggest improvements to the KG content.
Non-functional Requirements
Specific technical needs or specifications that proposals must meet.Must Have: Initial Knowledge Graph Deployment:
Deploy a scalable KG infrastructure capable of representing DeepFunding content that includes
entities (data objects) such as documentation, proposers, proposals and milestones, voters and votes
and labelled relationships between participants in the network.
The KG should be designed with future ecosystem-wide content integration in mind, allowing for scalability (compatible with nested and interlinked subgraphs) and the handling of a growing volume of data types and sources.
Reliable hosting solution for the KG for 1 year. Having a well-defined plan to host in a partner of SNET like Ocean or ICP would be a bonus.
Data Ingestion Process:
Ability to add/delete data to the KG in JSON-LD format, compatible with data from DeepFunding voting portal, and with flexibility to add new sources as required.
Include data cleaning mechanisms to ensure the accuracy and quality of the data ingested into the KG. (to be tested with export data from the test round).
Semantic and Social Network data structure to understand what has been said (Semantic) and who said what (Social Network).
Automated Data Pipelines for real-time data ingestion from a broader array of sources, ensuring data relevance by integrating live updates to the KG.
Data Enrichment Tools: Integrate NLP and ML tools for semantic analysis, automated categorization, tagging, and enrichment to uncover hidden relationships and enhance metadata.
Protected data and access management.
Nice to Have:
MetaData entry for Reputation scores for each data object (trustability).
MetaData entry for Flagging (3rd party application warnings on trustability).
Auditable log of interactions with and modifications to the KG.
Main evaluation criteria
Proposals will be evaluated on the following criteria:
Alignment with requirements and objectives: does the proposal meet the requirements and advances the objectives of the RFP.
Pre-existing infrastructure & risk mitigation: is the team starting from 0 or has already built part/all of the proposal, and are other risk mitigation factors at play.
Team Competence: does the team have relevant experience.
Cost: does the proposal offer good value for money.
Other resources
Our AI-platform is quickly evolving, especially in the area of providing API based 'knowledge nodes'. This is a bit of a moving target, but the internal singularityNET team will be available for help on onboarding and expert advice, where needed.
RFP Status
Feedback & Selection
The community and public are invited to view the full proposals and give feedback. During this time the RFP committee will doing their formal selection process to award winning proposals.
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Could you provide examples of interesting demos that have been developed using MeTTa so far? Additionally, what specific features of MeTTa do you recommend leveraging to create impactful demos
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arslantariq786
Aug 12, 2024 | 6:50 AMEdit Comment
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Could you provide examples of interesting demos that have been developed using MeTTa so far? Additionally, what specific features of MeTTa do you recommend leveraging to create impactful demos