Digitizing Engineering Intelligence: RDF-Powered Knowledge for Engineers

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.