kurtcagle

kurtcagle / shacl-methodology

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Describes a methodology for use with SHACL 1.2, including reifications

19
2
100% credibility
Found Mar 03, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Mermaid
AI Summary

A comprehensive methodology guide with diagrams for iteratively designing and validating structured data shapes using governance-first principles.

How It Works

1
🔍 Discover the Guide

You stumble upon this helpful guide while looking for ways to organize and check complex family trees of information.

2
📖 Explore the Big Picture

You read the friendly overview to grasp the key ideas like starting with planning who does what and using real examples.

3
🎨 See the Colorful Maps

You light up seeing the easy-to-follow diagrams that map out the whole journey like a roadmap for your project.

4
🏗️ Lay the Groundwork

You begin by deciding the scope, who's in charge, and simple naming rules to keep everything tidy from the start.

5
🔄 Build and Tweak in Loops

You sketch sample data, create basic structures, test them, get feedback, and loop back to improve until it feels right.

6
Handle Feedback Choices
📝
Update Examples

Go back to sketching better real-world samples.

🔧
Refine Structures

Tweak the main building blocks directly.

✅
Fix Tests

Strengthen the checking steps.

🎉 Celebrate Your Solid Plan

Your information organization system is complete, validated, and ready to grow with your needs.

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AI-Generated Review

What is shacl-methodology?

This repo delivers a governance-first, iterative methodology for designing ontologies with SHACL 1.2, including reifications for relationship-level constraints. It describes an exemplar-driven workflow—start with Turtle data sketches, cycle through taxonomy, shapes, validation, and reviews—tailored for enterprise knowledge graphs. Mermaid diagrams visualize the full process, from namespace strategy to LLM-assisted schema tweaks.

Why is it gaining traction?

Unlike OWL-focused guides, this methodology describes SHACL's data-shape-first approach with clear exit criteria, feedback loops, and SHACL 1.2 features like sh:reifierShape and sh:targetWhere. Developers grab it for the battle-tested patterns from eight years of real projects, plus guardrails for LLM prompts and CI-friendly test specs. The Mermaid visuals make complex phases like taxonomy-reification flows instantly graspable.

Who should use this?

Semantic engineers building enterprise RDF schemas who need to enforce governance before coding shapes. Knowledge graph teams shifting from OWL inference to SHACL constraints, especially with SHACL 1.2 processors like Apache Jena or pySHACL. Ontology designers handling stakeholder reviews and validation regressions in iterative cycles.

Verdict

Solid docs and diagrams make this a practical starting point for SHACL 1.2 workflows, despite low maturity—19 stars and 1.0% credibility score signal it's niche and early. Adopt if you're in RDF-heavy projects; otherwise, wait for more adoption or test it on a side gig.

(178 words)

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