Work

Projects

Website for the Unified Guidelines for the Creation of Semantic Models in the IoP

The guidelines for developing ontologies in the Internet of Production are now available as an interactive website. It includes full-text search with highlighted excerpts, a command palette, and an "Ask this guide" box for plain-English questions that point straight to the relevant passage. The running class/property example and the 42-ontology domain comparison from the Appendix are now explorable graphs instead of static diagrams. The Ontology Requirements, Competency Questions, and Class/Property/Individuals Definition templates are fillable directly on the page, autosave in your browser, and export to CSV, Markdown, or a ready-to-use OWL/Turtle file — and as you fill them in, your own classes and properties render as a live graph next to the book's example, with support for keeping multiple projects side by side. The site also includes a progress tracker for the 11-step workflow, dark mode, adjustable text size, and a citation/BibTeX section.

Unified Guidelines for Ontology Development for IoP

A practical, unified guideline for developing ontologies in the Internet of Production, letting Domain Experts and Knowledge Experts collaborate on semantic models without starting from scratch each time — built by studying and merging existing ontology-development methodologies.

IM Application Profiles

Application profiles mapping domain-specific information models onto the IDS Information Model, aligning independently developed ontologies so data described under different schemas can be matched, related, and exchanged consistently across a Data Space.

KGLab: Evaluating Ontology Alignment Quality on BioPortal

A study evaluating how trustworthy the automatically-generated mappings between biomedical ontologies on BioPortal really are, using structural and terminological similarity metrics to flag matches that look correct by label but diverge semantically, benchmarked against a reference alignment.

Semantic Interoperability in Data Spaces

Ongoing research into how Data Spaces achieve interoperability by grounding shared vocabularies in semantics rather than fixed schemas — using ontologies, controlled vocabularies, and mapping/matching techniques so participants from different domains (Mobility, Industry 4.0, Agriculture, Culture) can exchange and understand each other's data without agreeing on a single rigid format up front.