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.

- 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.

$30,000 USD

- 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.

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

- 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.

$30,000 USD

- 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.

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.

- 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.

$30,000 USD

- 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).

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.

- 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.

$30,000 USD

- 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).