>Open Source Legislation
A groundbreaking platform that transforms fragmented legislative data across global jurisdictions into a unified, machine-readable knowledge graph. By standardizing and contextualizing legal information, it enables researchers, civic tech organizations, and AI systems to navigate complex legal landscapes with unprecedented ease and insight.
>Problem & Solution
Problem
Legal professionals and researchers face a fragmented landscape of legislative data across jurisdictions, each with unique formats, structures, and access methods. This fragmentation creates significant barriers to comparative analysis, legal research, and the development of AI-powered legal tools. Without standardization, valuable legal information remains siloed and inaccessible for computational analysis.
Solution
Developed an adaptive legislative data platform that employs specialized scraping templates to extract, normalize, and enrich legal content from 52+ jurisdictions. The system transforms rigid hierarchical structures into a traversable knowledge graph with innovative "definition hub" architecture, cross-reference mapping, and vector embeddings—making complex legal relationships machine-readable and enabling powerful AI-driven legal analysis.
>Approach
Template-Based Scraping System
Developed a set of specialized scraper templates through extensive trial and error: flat scrapers for simple legislation, recursive scrapers for multi-page hierarchical content, and combination approaches for complex jurisdictions.
Definition Hub Architecture
Created 'definition hubs' as nodes attached to structure nodes, enabling leaf-to-root traversal to collect all applicable definitions at any point in the legislation, solving the complex problem of definition scope.
Reference Graph Transformation
Transformed legislation from a tree structure to a full graph by extracting and processing references, creating connections between semantically related sections that might be structurally distant in the original text.
>Technical Insights
Definition Hub System
Created a system to extract and organize legal definitions with their applicable scopes. By attaching definition hubs to structure nodes, the system enables leaf-to-root traversal to collect all relevant definitions at any point in the legislation, making complex legal context machine-readable.
Reference Graph Transformation
Transformed legislation from a tree structure to a full graph by extracting and processing cross-references. This created connections between semantically related sections that might be structurally distant, enabling powerful non-linear traversal essential for comprehensive legal analysis.
LLM-Optimized Legal Knowledge Graph
The combination of hierarchical structure with definition hubs, reference connections, and vector embeddings creates a knowledge graph that's uniquely suited for LLM-based legal reasoning, enabling AI agents to navigate legislation similar to human legal experts.
>Project Gallery
>Technologies
>Results
- Created a unified platform that standardizes legislative data across 52+ jurisdictions into a consistent, machine-readable format
- Developed a sophisticated knowledge graph that captures both hierarchical structure and semantic connections between legislative elements
- Built innovative systems for extracting and contextualizing legal definitions with proper scope management
- Demonstrated practical value through successful implementation in enterprise AI Engineering contract work
>Key Metrics
>Key Learnings
- Different jurisdictional websites require tailored scraping approaches
- Legal hierarchies can be effectively modeled as graph structures
- Definition scope and reference connections are critical for legal understanding
- Combining structured data with vector embeddings creates powerful AI-ready datasets
- Pattern recognition across different legislative formats leads to reusable templates
- Legal data requires both vertical (hierarchical) and horizontal (reference) navigation to be truly useful for analysis.
- Pattern recognition combined with LLM processing can extract structured information from unstructured legal text at scale.
- Democratizing access to legal information requires not just making text available, but transforming it into navigable, context-aware structures.