Composable Knowledge Graph Toolkit for AGI Systems

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Vinay Devabhakthuni
Project Owner

Composable Knowledge Graph Toolkit for AGI Systems

Expert Rating

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Overview

This proposal introduces the Composable Knowledge Graph Toolkit — a modular, developer-friendly framework for constructing, managing and reasoning over symbolic knowledge graphs in AGI systems. It is designed for seamless integration with OpenCog Hyperon and the MeTTa language, while supporting semantic web standards (RDF/OWL) and decentralized knowledge provenance (DIDs, IPFS). Unlike monolithic graph tools, this toolkit emphasizes modularity, symbolic runtime compatibility and developer-centric usability. It empowers AGI developers to build more transparent, traceable and trustworthy reasoning systems.

RFP Guidelines

Advanced knowledge graph tooling for AGI systems

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $350,000 USD
  • Proposals 39
  • Awarded Projects 5
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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

Our Team

Konma Labz is a multidisciplinary team specializing in symbolic AI, knowledge graph engineering and decentralized systems. We build modular, transparent and verifiable tools for AGI development, including RDF/OWL integration, MeTTa interoperability, graph visualization and zk-proof-based provenance. Our team blends systems engineering with human-centered design to deliver scalable, open-source infrastructure for explainable, trustworthy AGI.

Company Name (if applicable)

Konma Labz

Project details

Konma Labz

 Team Experience

Konma Labz is a systems research and engineering group focused on building modular infrastructures for knowledge processing, symbolic reasoning and decentralized intelligence. Our work bridges symbolic AI, data engineering and platform design — with a strong emphasis on explainability, compliance and long-term autonomy.

Relevant Capabilities

Knowledge Engineering & Ontology Workflows
We have deep expertise in building pipelines that transform data into structured, navigable knowledge — including:

  • RDF/OWL ingestion and transformation

  • Canonical graph normalization

  • Ontology alignment and conflict resolution

  • Knowledge provenance and trace modeling

Symbolic Reasoning System Design
Our engineering practice includes designing:

  • Domain-specific languages for symbolic abstraction

  • Graph-based reasoning workflows

  • Interoperable data representations between runtime logic and external schemas

Developer Tooling & SDKs
We build highly usable, modular SDKs and APIs that support:

  • DAG-based data structures and traversal

  • REST/gRPC services for graph manipulation

  • Real-time UIs for graph exploration and explainability

Provenance, Traceability & Verification
Our work includes:

  • IPFS and decentralized identity (DID) integration

  • Knowledge source tagging, versioning, and policy tracking

  • Experimental use of zkVMs ( RISC Zero) for proof-of-transformation pipelines

Platform and Systems Integration
We routinely build integration layers for:

  • Symbolic runtimes like MeTTa

  • Graph databases and Atomspace-like environments

  • Federated and decentralized knowledge systems

Approach

We combine research-grade knowledge frameworks with engineering-grade systems design. Our priority is building transparent, modular and verifiable tools that enable developers and researchers to reason, trace, and collaborate over symbolic knowledge in decentralized environments.

Makers of SkyBrain Neurotech, A DF funded BCI based Startup

Website: https://www.konma.io/r-d

 

Open Source Licensing

Apache License

License Details

The Composable Knowledge Graph Toolkit will be released under the Apache License 2.0, a permissive open-source license that encourages adoption, modification and contribution.

This license allows:

  • Free use in commercial and non-commercial projects

  • Modification and redistribution under compatible terms

  • Protection for contributors via explicit grant of patent rights

Background & Experience

Konma Labz is a research and engineering collective focused on building modular tools for knowledge representation, symbolic reasoning and AI transparency. Our team combines experience in knowledge graphs, semantic data pipelines and intuitive developer tooling. We’ve worked across domains like ontology mapping, graph visualization and explainable AI. With backgrounds in systems design, human-centered computing and decentralized tech, we’re well-equipped to deliver open, interoperable solutions that align with the goals of the SingularityNET ecosystem. Our emphasis on usability, traceability and integration ensures practical impact for AGI developers and researchers alike.

Links and references

https://konmadao.notion.site/107468b438dc80cbb1c0db5ecd953c77?v=fff468b438dc81e0a56a000c8ac0e0c6&pvs=4

Describe the particulars.

SkyBrain Neurotech

Proposal Video

Not Avaliable Yet

Check back later during the Feedback & Selection period for the RFP that is proposal is applied to.

  • Total Milestones

    4

  • Total Budget

    $120,000 USD

  • Last Updated

    20 May 2025

Milestone 1 - Ontology Ingestion & Translation Engine

Description

Timeline: Month 1–1.5 Objective: To build a foundational pipeline that enables the ingestion of external knowledge sources (RDF/OWL) and translates them into symbolic graph structures compatible with AGI reasoning environments such as MeTTa.

Deliverables

- Ontology Loader Module: Converts RDF/OWL ontologies into MeTTa atoms, retaining class-property-instance logic. - Schema Validator & Structure Mapper: Ensures that imported ontologies are structurally consistent and compatible with internal graph logic. - CLI Tool (v1): Command-line interface to upload, transform and validate knowledge inputs.

Budget

$30,000 USD

Success Criterion

- At least 10 standard ontologies (e.g., FOAF, DBpedia) successfully imported and converted to symbolic form. - Conversion pipeline handles 1000+ nodes per ontology with no data loss. - CLI tool provides usable outputs and generates validation logs. - Initial unit tests cover ≥80% of code paths.

Milestone 2 - Canonicalization & Semantic Export Modules

Description

Objective: To implement tools that normalize and unify imported knowledge structures and support seamless bidirectional transformation between symbolic representations and semantic web standards.

Deliverables

- Graph Canonicalizer: Normalizes symbolic knowledge (alias merging, structural cleanup, redundant node pruning). - Export Tools (JSON-LD, Turtle): Converts processed MeTTa graphs back to RDF/OWL-compatible formats. - Format Validation Test Suite: Ensures consistency of structure and content across import-export cycles.

Budget

$30,000 USD

Success Criterion

- Canonicalizer successfully normalizes at least 5 diverse imported ontologies with different schema styles. - Round-trip conversion (RDF → MeTTa → RDF) preserves ≥95% of structural fidelity. - Graph export validated against RDF schema compliance tools. -Test suite confirms semantic equivalence across ≥10 transformation scenarios.

Milestone 3 - Runtime Integration with Symbolic Execution

Description

To enable real-time interaction with symbolic knowledge graphs within AGI runtimes by integrating MeTTa execution logic and AtomSpace memory interfaces, supported by a developer-accessible SDK.

Deliverables

- MeTTa Runtime Adapter: Allows MeTTa programs to load symbolic graphs and execute reasoning steps on them. - OpenCog AtomSpace Bridge: Enables read/write graph operations within AtomSpace to reflect memory and state changes. - Developer SDK (Rust or Python): Includes API bindings, code examples and helper functions for integrating graph logic into AGI tools or pipelines.

Budget

$30,000 USD

Success Criterion

- Graphs are dynamically loaded into MeTTa runtime in under 500 ms for 1000+ node DAGs. - AtomSpace bridge successfully reads and updates at least 3 symbolic memory graphs in runtime simulations. - SDK includes publicly documented APIs, installation instructions, and usage notebooks. - Developers can use SDK to build and execute a complete graph-driven reasoning flow (input → process → output).

Milestone 4 - Graph Visualization + Provenance Metadata Layer

Description

To provide user-facing tools for graph exploration and introduce decentralized provenance tracking using IPFS and DIDs, ensuring transparency, traceability and trust in symbolic reasoning workflows.

Deliverables

- Web-based Graph Explorer: UI built with React + Cytoscape.js to visualize, navigate and inspect symbolic DAGs. - Trace & Query Features: Supports search, filter and highlight paths or subgraphs (e.g., "show all nodes related to concept X"). - Provenance Metadata Layer: Tags each node with IPFS/DID metadata capturing source, creator and transformation lineage. - Graph Signing Utility: Allows contributors to cryptographically sign knowledge inputs or revisions using DID methods.

Budget

$30,000 USD

Success Criterion

- DAG explorer renders 1000-node graphs in under 1 second, with real-time filtering enabled. - Provenance metadata is attached to ≥90% of knowledge entries and can be resolved via IPFS. - Signing utility verifies graph contribution identity using at least 2 DID methods (e.g., key pair, web DID). - Full knowledge trace available for 3 complete reasoning chains (i.e., input → decision → output with full metadata lineage).

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