# Feature Idea: AtomSpace/MeTTa-Inspired Knowledge Graph for Incremental Learning

## Overview

Introduce a flexible, graph-based knowledge representation layer inspired by AtomSpace and MeTTa (OpenCog) to support incremental learning and applied cognition in the LinkedIn SSI Booster. This layer will enable the system to represent, integrate, and reason over new knowledge as it is gathered from user interactions, content curation, and external sources.

## Problem Statement (Project Context)

Current persona and domain knowledge are stored as lists or dicts, which limits:

- Expressiveness of relationships (beyond simple key-value or flat facts)
- Ability to incrementally add, link, and update new knowledge
- Traceability and explainability of how knowledge is used or cited
- Advanced reasoning (pattern discovery, analogical inference, etc.)

## Proposed Solution

- Implement a knowledge graph subsystem (initially in Python, e.g., using `networkx`)
- Encode new facts, experiences, and relationships as nodes and typed links (hypergraph style)
- Attach metadata (source, confidence, timestamp) to each node/link
- Support pattern matching and graph traversal for retrieval and reasoning
- Integrate with the learning/memory subsystem: when new knowledge is learned, add/update the graph
- Use graph queries to surface relevant knowledge for grounding, post generation, and explanations

## Expected Benefits (Project User Impact)

- More expressive and flexible knowledge integration
- Improved explainability and traceability of answers and generated content
- Foundation for advanced reasoning and applied cognition features
- Easier to extend and adapt as new types of knowledge are encountered

## Technical Considerations (Project Integration)

- Start with a simple in-memory graph (e.g., `networkx`); consider graph DB (Neo4j) if scale/complexity grows
- Define a minimal schema for node/link types (e.g., Person, Project, Skill, Fact, Event, etc.)
- Ensure compatibility with existing persona/domain fact ingestion
- Add utilities for graph updates, queries, and serialization
- Optionally, experiment with MeTTa-like pattern matching for reasoning

## Project System Integration

- Integrate with avatar learning subsystem: new knowledge is added as graph nodes/links
- Retrieval layer can use graph traversal to find relevant facts for grounding
- Optionally, expose graph queries for debugging, analytics, or advanced user queries

## Initial Scope

- Prototype: represent new learned facts as nodes/links in a Python graph
- Add/update graph as new knowledge is acquired
- Retrieve relevant subgraphs for grounding and explanations
- Document the schema and update workflow

## Success Criteria

- New knowledge is represented as graph nodes/links and can be incrementally updated
- System can retrieve and explain how a fact was used or cited
- Integration does not break existing deterministic fact retrieval
- Documentation and example queries provided
- All tests pass (see testing-and-dev.md for coverage)
- Feature is fully documented in README and support docs
