KnowledgeGraphEnricher

# KnowledgeGraphEnricher

You are a coding agent working on KnowledgeGraphEnricher -- An AI-powered tool that automatically enriches knowledge graphs with new information from the web, academic databases, and user contributions.

## Foundational Axiom

Existing approaches to ai-powered tool fail because they optimize for the common case while ignoring structural constraints; KnowledgeGraphEnricher makes those constraints first-class.

## Confirmation Gate

Before writing any code, confirm:
1. You have read `spec.org` in this directory.
2. You understand the build order and current step.
3. You will not skip a failing acceptance test.

## What You Are Building

- Core system: An AI-powered tool that automatically enriches knowledge graphs with new information from the web, a
- User interface: define project scope and requirements
- Data layer: design data model

## Anti-Goals

- **General-purpose platform**: KnowledgeGraphEnricher solves a specific problem, not a platform for all problems in this domain. Building a platform leads to feature sprawl and loss of core value.
- **Manual-first workflow**: Do not build processes that require constant human intervention for routine operations. If a human must babysit it, the automation has failed.
- **Demo-ware**: Do not optimize for impressive demos at the cost of production reliability. A system that works in a demo but fails under real conditions is worse than no system.

## Build Order

1. Define Project Scope and Requirements (P5)
2. Design System Architecture (P4) -- (depends on: Define Project Scope and Requirements)
3. Design Data Model (P4) -- (depends on: Define Project Scope and Requirements)
4. Develop Web Crawling and Data Extraction Components (P3) -- (depends on: Design System Architecture, Design Data Model)
5. Develop Knowledge Graph Enrichment and Reasoning Components (P3) -- (depends on: Design System Architecture, Design Data Model)
6. Develop User Interface and Contribution Mechanisms (P3) -- (depends on: Design System Architecture)
7. Implement Security and Access Control (P3) -- (depends on: Design System Architecture)
8. Set up Continuous Integration and Deployment (P2)
9. Write Documentation and User Guides (P2) -- (depends on: Define Project Scope and Requirements)
10. Conduct Testing and Quality Assurance (P2) -- (depends on: Develop Web Crawling and Data Extraction Components, Develop Knowledge Graph Enrichment and Reasoning Components, Develop User Interface and Contribution Mechanisms)

### Failure Handler

If an acceptance test fails, STOP. Document what failed, what you tried, and the blocker. Do not proceed to the next step. Surface the failure as a CPRR refutation candidate.

## Open Conjectures

- **C-001**: KnowledgeGraphEnricher can deliver its core value proposition as described
  - Falsification: Integration test of end-to-end workflow fails to produce expected output
- **C-002**: AI/ML components achieve sufficient accuracy for production use
  - Falsification: Model accuracy on held-out test set falls below domain-specific threshold
- **C-003**: System meets real-time latency requirements under load
  - Falsification: P95 latency exceeds target under simulated production load
- **C-004**: Security implementation meets compliance requirements
  - Falsification: Penetration test or security audit reveals critical vulnerability

## Stack

- Python (default)

## Success Criteria

1. All build steps completed with passing acceptance tests.
2. End-to-end workflow demonstrates core value: An AI-powered tool that automatically enriches knowledge graphs with new information from the web, a.
3. All open conjectures either confirmed with evidence or refuted with data.
4. System deployed and accessible to domain professionals who need specialized tooling.