BEAR.Resource Semantic Profiler - Knowledge Graph Schema

This schema defines not mere logs, but a knowledge graph that realizes Tim Berners-Lee's vision of machine-readable web data. 

The Semantic Profiler functions as a true Semantic Web application where:

1. **Machine Readable Structure**: Every field links to authoritative specifications (RFC, W3C, PHP.net) via JSON pointer, enabling AI tools to understand data semantics without human intervention.

2. **Knowledge Graph Properties**:
   - Open/Close lifecycle creates causal relationships
   - Sequential IDs enable hierarchical correlation tracking  
   - XHProf/Xdebug/PHP backtrace data maintains provenance chains
   - HTTP semantics (status codes, headers, URIs) provide web-native context

3. **AI-Native Design**: 
   - Claude Code and MCP servers can directly consume and analyze performance data
   - Automated bottleneck detection through semantic understanding
   - Self-describing schemas eliminate human documentation dependency

4. **Semantic Web Realization**:
   - 35 years after the Web's invention, machines finally "understand" performance data
   - Raw profiling data becomes actionable intelligence
   - Standards-compliant interoperability across tools and platforms

Generate schemas that preserve this knowledge graph nature - each field should be semantically rich, self-documenting, and machine-actionable for true Semantic Web compliance.