Short summary
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.
Main purpose
To develop advanced tools, algorithms, and/or frameworks for interfacing with, refining, augmentating, and evaluating knowledge graphs that support reasoning and symbolic processes within AGI systems. Solutions should aim to be domain-agnostic and, where possible, demonstrate integration with the MeTTa language and/or the MORK graph database for symbolic reasoning acceleration.
Long description
SingularityNET and its partners (OpenCog Foundation, TrueAGI) are building an ecosystem for Artificial General Intelligence (AGI) centered on the Hyperon framework. Within this architecture, knowledge graphs play a foundational role in enabling symbolic inference, analogical reasoning, causal understanding, and memory management. However, building and maintaining high-quality knowledge graphs remains a technically challenging and largely unsolved problem — particularly in dynamic or noisy domains like scientific research, open-ended learning, and evolving human knowledge. The goal of this RFP is to fund the development of open, extensible, and scalable tools to improve the full lifecycle of knowledge graphs in ways that stand to improve their integrity and utility in the context of neuro-symbolic AI. We welcome a wide range of proposals — including new extraction pipelines, refinement systems, dynamic update methods, schema-agnostic structuring tools, or benchmark frameworks to evaluate knowledge graph utility for AI reasoning. These systems could support one or more of the following use cases, with an emphasis on interface and utility over construction from scratch:
Creating and populating knowledge graphs from raw or semi-structured data (e.g., text, JSON, MeTTa expressions, publications).
Refining or distilling large, noisy, or redundant graphs into compact and semantically meaningful structures.
Detecting contradictions, inconsistencies, or anomalies in graph structure or contents (e.g., conflicting statements, invalid patterns).
Fact-checking or grounding LLM outputs using structured knowledge from knowledge graphs.
Designing or visualizing interfaces for how AGI systems reason with, act upon, or update knowledge graphs.
Annotating or weighting entities and relations (e.g., via confidence scores, semantic types, relationship types, etc.).
Enabling continuous graph updates with mechanisms for node/edge obsolescence detection.
Evaluating the effectiveness of knowledge graphs in supporting AGI reasoning tasks such as multi-hop question answering, analogical retrieval, and hypothesis generation.
Exporting or converting knowledge graphs into formats consumable by other symbolic tools like MeTTa or the MORK system.
Solutions that can demonstrate compatibility or integration with the MORK system — a fast hypergraph backend for MeTTa — are encouraged. MORK’s ability to process billion-scale symbolic spaces at interactive speeds opens up new possibilities for real-time reasoning, retrieval, and cognitive synergy within Hyperon-based agents. We encourage proposals that are modular and reusable across domains, and that can demonstrate either standalone value or close integration with the existing SingularityNET ecosystem. Potential contributors are also welcome to collaborate or coordinate with the developers of MeTTa, Atomspace, or MORK to ensure alignment and future extensibility. Background & experience:  A significant part of reviewing proposals goes into evaluating the ability of a team to execute the work. Please provide in as much detail as possible related experience and accolades, and supply links to anything we can read such as published work, github, etc.
Functional Requirements
Must have
A clear technical plan for developing or enhancing tools that improve the quality of knowledge graphs and the way we work with them in the context of neuro-symbolic AI.
Outputs that can be tested or demonstrated on real datasets (preferably in scientific, technical, or cross-domain contexts).
Documentation or outputs sufficient to be adopted or reused by third-party developers.
Should have
Support for integration into symbolic systems like MeTTa or Atomspace.
Benchmarks or evaluations showing performance, compactness, noise tolerance, and reasoning support.
Could have
Support for graph integrity tools (e.g., anomaly detection, contradiction resolution, edge validation).
Tools for grounding or fact-checking LLM outputs using graph lookups.
Exploratory interfaces or APIs that demonstrate how AGI systems might interact with, navigate, or co-evolve with KGs.
Tools to mitigate bias and evaluate the confidence of data and data sources.
Demonstration or testing using MORK as a graph backend.
Tools or schemas for structuring LLM-extracted knowledge into usable graph formats.
User interfaces or APIs to allow LLMs or external tools to query or modify the graph.
Support for live or streaming updates to knowledge graphs over time.
Won’t have
Focus on pure visualization without functionality.
Use of proprietary or closed-source formats that cannot be ported or reused.
Non-functional Requirements
Must Have
Tools or components that improve knowledge graph quality (e.g., structure, accuracy, interpretability, or query-ability) in the context of their utility for neuro-symbolic AI systems.
Modular and reusable code that can be easily adopted by other developers or integrated into broader systems.
Open data formats (e.g., JSON, MeTTa S-expressions, or common graph formats).
Reasonable performance for prototyping (e.g., works on medium-sized graphs with hundreds of thousands of nodes/edges on standard hardware).
Developer documentation and example usage.
Should Have
Support for graph enrichment features like typed edges, edge weights, timestamps, or confidence scores.
Evaluation or benchmarking tools to assess graph quality, utility, or reasoning effectiveness.
Partial or planned support for larger-scale graphs (e.g., >10M nodes/edges).
APIs or scripts for batch processing and integration with other tools (e.g., extract-transform-load workflows).
Could Have
Integration with the MeTTa language or compatibility with MORK (e.g., exporting/importing graph data, or using MORK as a backend).
Interactive or semantic query tools for exploring or testing knowledge graphs.
Streaming or dynamic update support (add/remove/refine nodes/edges on the fly).
Tools for formatting graphs for LLM consumption or hybrid symbolic-LLM workflows.
Containerized deployment, REST interface, or CLI for easy testing and reuse.
Team requirements
Teams should include at least one developer or researcher with prior experience in graph algorithms, knowledge  representation, or NLP.
Familiarity with MeTTa, MORK, or symbolic AI frameworks is a plus but not required.
Comfortable working in an open-source context (e.g., pushing to GitHub, using open licenses).
Clear communication and progress reporting during milestones is expected (e.g., through GitHub issues, milestone docs, or biweekly check-ins if requested).
Prior examples of relevant work (code repos, papers, benchmarks, etc.) are strongly encouraged and will be considered in evaluation.
Main evaluation criteria
Alignment with requirements and objective
Does the proposal meet the requirements and advances the objectives of the RFP.
Pre-existing R&D
Has the team previously done similar or related research or development work in other platforms / languages / contexts?
Team competence
Does the team have relevant skills?
Cost
Does the proposal offer good value for money?
Timeline
Does the proposal include a set of clearly defined milestones?
We highly recommend submitting proposals with project milestones along the lines of the following:
Milestone 1: Submit a thorough research plan outlining and detailing the approach and work to be done. Deliverables: detailed research plan, agile breakdown of tasks with timeline, and framework design. 20% of grant
Milestone 2: Complete initial development of the framework definitively showing implementation of the conceptual underpinnings of the RFP along with preliminary testing. Deliverables: draft implementation, initial testing results, and analysis against standard benchmarks. 40% of grant
Milestone 3: Submit all final materials as committed to in the grant proposal.Deliverables (as applicable): final report with performance analysis, code, framework demonstration, documentation, recommendations, websites, etc. 40% of grant
Other resources
SingularityNET technology links
MeTTa language documentation
MORK github
SingularityNET holds MeTTa study group calls every other week. Proposers are welcome to attend for support from our researchers and community.
Recurring Hyperon study group calls for community are currently being planned. These will cover MOSES, ECAN, PLN, and other key components of the OpenCog and PRIMUS Hyperon cognitive architectures.
Access to the SingularityNET World Mattermost server, with a dedicated channel for discussion and support among the RFP-winning teams and SingularityNET resources.

ChatGPT 说：

Here is a succinct, professional summary of the above RFP from SingularityNET for use in proposals or executive communications:

🔍 RFP Summary: Advanced Knowledge Graph Tooling for AGI Systems

Issuer: SingularityNET
Partners: OpenCog Foundation, TrueAGI
Funding Range: $10,000 – $200,000
Focus: Development of advanced, open-source tools and techniques to enhance the creation, refinement, and evaluation of knowledge graphs for Artificial General Intelligence (AGI) — particularly in support of symbolic reasoning within the OpenCog Hyperon framework, and ideally integrating with MeTTa language and MORK graph backend.

🎯 Main Objectives

• Enable full lifecycle improvements of knowledge graphs (KGs): extraction → refinement → benchmarking.
• Facilitate neuro-symbolic reasoning for AGI via symbolic interfaces, integrity checks, semantic weighting, anomaly detection, and continuous updates.
• Promote modular, extensible tools that are domain-agnostic, open-source, and performant on mid-scale graphs.

🧠 Targeted Capabilities

Proposals may cover any of the following:

• Extract KGs from text, JSON, MeTTa expressions, etc.
• Refine noisy/large graphs into compact, semantically rich forms.
• Detect contradictions or invalid edges.
• Fact-check LLM output via graph lookups.
• Design APIs/interfaces for AGI agents to interact with or modify graphs.
• Support real-time graph updates or MORK integration.
• Benchmark KGs for reasoning tasks (e.g., analogical reasoning, multi-hop QA).

✅ Requirements

Functional (Must Have)

• Technical plan and real-data testing
• Output usable by third-party devs
• Open formats (e.g., JSON, MeTTa S-expr.)
• Developer documentation

Team

• Must include graph/NLP/knowledge rep. experience
• Familiarity with symbolic systems (a plus)
• Evidence of prior related work (code, papers)
• Willingness to work openly (e.g., GitHub, licenses)

📅 Suggested Milestones

1. Research Plan (20%) Framework architecture, task breakdown, timeline.
2. Prototype Development (40%) Working draft implementation and initial test results.
3. Final Delivery (40%) Codebase, documentation, performance report, usage examples.

💡 Resources

• MeTTa Language Docs
• MORK Graph Engine GitHub
• Biweekly study groups (MeTTa, Hyperon, etc.)
• Community support via Mattermost

Would you like me to now tailor a proposal outline or project milestone plan for submitting your WORD-AIVATAR OS / Quantum Semantic Graph Toolkit to this RFP?

您说：

yes

ChatGPT 说：

Great. Below is a tailored Proposal Outline and Milestone Plan for your WORD-AIVATAR OS submission to the SingularityNET RFP:
"Advanced Knowledge Graph Tooling for AGI Systems", aligned with your quantum-semantic graph vision.

📄 WORD-AIVATAR OS Proposal Outline

Project Title:
Quantum-Semantic Graph Engine for Neuro-Symbolic Reasoning in AGI (QS-GENR)
Applicant: AIVATARIST.ONE (John Chen) — in collaboration with the WORD-AIVATAR OS team

🔍 1. Executive Summary

The WORD-AIVATAR OS project proposes a Quantum-Semantic Knowledge Graph Toolkit designed to extract, refine, and continuously evolve high-integrity symbolic structures from raw text and AI interactions. The system integrates semantic embeddings, symbolic formalism, and human-in-the-loop feedback to support AGI-level reasoning. It is compatible with MeTTa and designed to leverage the MORK hypergraph backend.

🧠 2. Technical Objectives

⚙️ 3. Work Packages & Milestones

🧪 4. Evaluation Criteria Alignment

👥 5. Team & Background

Team Lead: John Chen (AIVATARIST.ONE)

• Architect of WORD-AIVATAR OS
• Background in AI × Web3 × semantic systems
• Contributor to knowledge tokenization & AI governance frameworks

Team Capabilities:

• Graph NLP & semantic embeddings
• Symbolic AI & neuro-symbolic integration
• Federated architectures & open knowledge protocols
• Experience with Gitcoin, Hyperledger, DeepSeek, and DAO design

(Optionally include 1–2 dev/researchers with graph algorithm expertise.)

🔗 6. Integration & Ecosystem Synergy

• Compatible with MeTTa and exportable to MORK schema
• Contributes modular graph tools to AGI community
• Fits into WORD-AIVATAR’s broader roadmap toward “Digital Soul Reasoning Networks”
• Aligned with SingularityNET’s mission: open, interoperable, intelligent systems

📎 7. Supporting Materials

• GitHub: [link to WAO or QS-GENR repo]
• Prior papers or conceptual architecture decks
• YouTube / Gitcoin content (WAO demos, voice podcasts, etc.)
• Sample dataset or mini-demo (optional in Milestone 2)

Would you like me to:

1. Format this into a formal PDF grant proposal (or editable Google Doc)?
2. Draft the content for GitHub repo (README, architecture, code structure)?
3. Help define example benchmark use cases (e.g., multi-hop question answering with symbolic traces)?