The first milestone will deliver a functional prototype of our noise reduction framework for knowledge graphs. This framework will implement our novel approach that leverages LLM-based noise detection combined with knowledge distillation techniques to identify and remove redundant or low-value nodes. The prototype will demonstrate significant improvements in graph quality while maintaining essential information integrity across multiple test datasets.

- A working prototype implementation of the noise reduction framework with documented API - Technical report detailing the architecture algorithms and comparative analysis against TF-IDF spectral sparsification approaches and other - Demonstration of the framework on at least two distinct knowledge graph datasets with varying characteristics

$30,000 USD

The framework will achieve 10-15+% reduction in graph size while maintaining 95% or higher accuracy on standard knowledge graph completion tasks compared to the original graph. Performance metrics will be documented across multiple domains to demonstrate domain-agnosticity.

This milestone focuses on developing a domain-agnostic entity and relationship extraction system capable of forming hierarchies from flat lists and graphs or transforming flat textual data into rich ontological structures. The system will incorporate advanced Named Entity Recognition semantic relationship classification and other techniques to identify complex relationships between entities. We will develop methods to assign confidence scores to extracted relationships enabling probabilistic reasoning over the knowledge graph.

- A deployable entity and relationship extraction system with comprehensive documentation - Evaluation report comparing our system against state-of-the-art extraction methods

$25,000 USD

The system will demonstrate the ability to extract entities and relationships across at least several distinct domains with F1 scores exceeding current state-of-the-art methods. The system will successfully assign meaningful confidence scores that correlate with human judgment.

We will develop a comprehensive benchmark suite for evaluating knowledge graph quality specifically designed for retrieval and reasoning tasks. This suite will measure multiple dimensions including query latency answer quality path interpretability and robustness to noise. The benchmark will include complex reasoning tasks that go beyond simple factual queries to assess reasoning capabilities more relevant to AGI systems.

- Open-source benchmark suite with documentation and easy setup process - Dataset collection spanning multiple domains for standardized evaluation - Draft of a methodology paper describing benchmark design principles and evaluation metrics

$22,500 USD

The benchmark will successfully differentiate performance between various knowledge graph approaches on complex reasoning tasks with statistical significance.

This milestone will deliver integration capabilities between our knowledge graph technologies and MeTTa and MORK compatible with the Hyperon framework.

- Integration libraries for connecting our knowledge graph tools with MeTTa and Hyperon - Example applications demonstrating end-to-end reasoning capabilities - Performance analysis comparing reasoning tasks before and after knowledge graph refinement

$10,000 USD

The integration will enable faster query processing on complex reasoning tasks compared to unrefined knowledge graphs. We will demonstrate successful multi-hop reasoning on at least three use cases that were previously intractable due to knowledge graph noise or inefficiency.