Overview
This use case shows how combining AI-driven data extraction with RDF-based semantic modeling transforms engineering operations. By automating P&ID intelligence into intuitive knowledge graphs, engineers gain faster, safer, and more context-aware decision-making capabilities.
Challenge
Engineering teams struggle with fragmented, manual data workflows that slow decisions and increase risk.
- Manual extraction of enterprise-wide data from P&ID diagrams is slow and error-prone.
- Aligning entities with industrial and custom ontologies is complex.
- Engineers lack intuitive views for context-driven decision-making.
Solution
- Automate data extraction with AI-powered QA/QC workflows for accuracy.
- Use RDF, ontologies and reasoning for standard-compliant semantic modeling for mapping data, contextualizing and entity resolution.
- Visualize and analyze enriched data via Neo4j knowledge graphs for usability.
Key Benefits
- Engineers quickly access system insights for isolation planning.
- Scalable, interoperable data unlocks upstream/downstream analysis.
- Semantic structure improves safety, efficiency, and planning.
