Evaluate the current KNAI infrastructure and define a detailed plan for enhancing knowledge graph tools particularly in alignment with symbolic reasoning integration.

Comprehensive assessment of KNAI’s existing knowledge graph pipeline (data ingestion structuring Neo4j conversion). Identification of gaps in MeTTa/MORK compatibility and symbolic reasoning capabilities. Detailed research plan outlining enhancements to graph refinement contradiction detection and confidence scoring systems. Agile task breakdown with timelines dependencies and resource allocation.

$20,000 USD

The research plan is validated by technical stakeholders. Identified gaps align with RFP requirements for knowledge graph quality and symbolic reasoning support. Task breakdown is integrated into the project roadmap and approved for execution.

Develop preliminary tools to refine large noisy knowledge graphs improving their semantic accuracy and utility for AI reasoning tasks.

Implementation of anomaly detection algorithms to identify contradictions and inconsistencies within knowledge graphs. Prototype confidence scoring system for entities and relationships based on data source reliability and consistency checks. Integration tests using real-world datasets (e.g. scientific literature medical research) to validate tool performance. Performance benchmarks comparing refined vs. raw graphs across metrics like query accuracy noise reduction and reasoning efficiency.

$25,000 USD

Tools demonstrate measurable improvement in graph quality and consistency. Benchmarking results show statistically significant enhancement in graph usability for downstream AI applications . Deliverables are documented and ready for integration into subsequent development phases.

Implement streaming updates and obsolescence detection mechanisms in KNAI’s knowledge graph system to ensure long-term relevance and adaptability.

Streaming update API enabling real-time addition removal or refinement of nodes and edges. Obsolescence detection logic based on timestamps usage patterns and confidence decay models. Integration with KNAI’s decentralised storage layer (IPFS) to support distributed persistent knowledge updates. Testing on evolving datasets such as news articles financial reports and dynamic research fields.

$25,000 USD

API supports continuous updates without compromising graph integrity. Obsolescence detection logic accurately identifies outdated or irrelevant knowledge entries. The system demonstrates scalability and responsiveness under real-time data loads.

Ensure full compatibility between KNAI’s knowledge graphs and OpenCog’s symbolic reasoning frameworks—MeTTa and MORK.

Conversion tools for exporting KNAI graphs into MeTTa S-expressions for symbolic processing. Integration with MORK as a backend for high-speed symbolic inference and cognitive synergy. Benchmarking against standard reasoning tasks such as multi-hop question answering and analogical retrieval. Developer documentation and example use cases demonstrating integration workflows

$30,000 USD

Knowledge graphs are successfully processed and executed within MeTTa and MORK environments. Reasoning benchmarks meet or exceed expected thresholds for AGI-relevant tasks . Integration documentation is complete and accessible to external developers.

Conduct final testing documentation and reporting to ensure deliverables meet all functional and non-functional requirements of the RFP.

Final performance report analyzing all developed tools including refinement update and symbolic integration modules. Publicly accessible code repository containing open-source implementations and test suites. User documentation tutorials and example use cases for adoption by third-party developers and researchers. Presentation of findings to the SingularityNET community and other key stakeholders.

$20,000 USD

All deliverables pass peer review and stakeholder validation. Codebase is well-documented, reusable, and adheres to open-source standards . Final report and presentation receive positive feedback from the broader AGI and neuro-symbolic AI communities.