https://drive.google.com/file/d/1vzYTsg6Y05Vn15W2uhBifzrJ58fQaYIr/view?usp=share_link

Organizations increasingly operate across two graph paradigms: RDF/OWL for standards-based semantics and validation, and property graphs for visualization, operational queries, and graph analytics. Bridging these worlds typically means one-way migrations that silently drop semantics, or brittle mappings that drift as ontologies evolve.

This hands-on workshop equips attendees to perform high-fidelity, bidirectional RDF-to-property-graph transformation using a four-stage E-I-L-R pipeline: Extract RDF assets and validate with SHACL; Interpret into a property-graph projection with explicit mapping modes; Load into a property graph environment; then Reinterpret back into standards-compliant RDF with provenance intact.

Attendees will work through each pipeline stage interactively using a guided web application, with curated sample datasets provided. Participants will also be welcome to bring their own RDF. Each stage pairs a brief conceptual introduction with a hands-on exercise, so attendees build intuition for mapping decisions, blank-node handling, validation gates, and fidelity contracts as they go. Some prior RDF and property graph experience is assumed, but the workshop will introduce the necessary foundations before each exercise in any case.

The pipeline and tooling presented were developed by Enterprise Knowledge through real client engagements where RDF serves as the semantic source of truth and property graphs provide the consumption layer for downstream AI and exploration.

Learning Objectives

Extract RDF from multiple serialization formats and apply SHACL validation gates to enforce data quality before transformation.

Configure interpretation mapping modes (labels, nodes, hybrid) and understand how each affects downstream query patterns and semantic clarity.

Load a property graph from the interpreted projection and verify structural fidelity against the source ontology.

Reinterpret a property graph back into standards-compliant RDF, preserving provenance and detecting semantic drift.

Define a transformation fidelity contract that specifies what must be preserved, what may be projected, and how to prove it.