JSON-LD is everywhere: Google Search, Verifiable Credentials, schema.org, knowledge graphs. It's a W3C standard used on billions of web pages. And it has serious gaps: no built-in validation, security vulnerabilities in context resolution, no streaming support for large datasets, and zero affordances for the AI/ML workloads that increasingly consume it.
I conducted a deep gap analysis of JSON-LD and identified 26 specific improvements across security, performance, validation, developer experience, and AI integration. Then I started building jsonld-ex - an extended JSON-LD library for Python that implements these fixes.
In this session, I'll walk through the problems with live examples (yes, I'll break JSON-LD on stage), then show how jsonld-ex addresses them:
What you'll see live:

Context injection attacks against standard JSON-LD processors: and how jsonld-ex prevents them
SHACL/ShEx shape validation integrated directly into the processing pipeline
Confidence scores and provenance tracking baked into JSON-LD nodes: critical for AI systems consuming linked data
A working MCP (Model Context Protocol) server that lets AI agents query and validate knowledge graphs through jsonld-ex

What you'll walk away with:

Understanding of JSON-LD's real-world limitations beyond the spec
Practical techniques for securing JSON-LD in production APIs
How linked data and AI agents intersect through MCP
A Python library you can extend or contribute to

If you work with APIs, knowledge graphs, semantic web, or AI systems that consume structured data: this talk shows you what's broken under the hood and how to fix it.