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Overview

Designing high-quality ontologies that are Findable, Accessible, Interoperable, and Reusable (FAIR) remains a key challenge in practice. This tutorial introduces a fully integrated workflow for principled ontology engineering based on the Simplified Upper Level Ontology (SULO), a lightweight upper-level ontology designed to guide conceptual representation. Through a hands-on, end-to-end examples, participants will (re)conceptualize, formalize, and automatically reason over the pizza domain using SULO design patterns in a notebook setting. Participants will use the OntoStart platform to publish their own ontology in a FAIR manner. The tutorial is aimed at students, researchers, and practitioners building domain ontologies. Participants will learn methodology and leave with a practical skills to bootstrap their own FAIR ontology projects.

Objectives

This tutorial addresses a persistent gap in applied ontology: the absence of accessible, end-to-end workflows for producing FAIR, modular, reusable ontologies with high conceptual and engineering quality. The tutorial objectives are to:

Target Audience

The tutorial aims to balance theory with hands-on ontology engineering over the pizza domain. The tutorial is pitched at a basic to intermediate level; no prior knowledge of upper level ontologies or OWL is required. Attendees that have knowledge of python may choose to implement the tutorial with python, otherwise attendees will be expected to run Protege.

Learning goals

The learning goals of the tutorial are twofold:

  1. Develop competency in the use of SULO to conceptualize and formalise a domain
    • Explain the role of an upper-level ontology and apply SULO’s categories and design patterns to conceptualize a domain.
    • Distinguish parts, features, roles, capabilities, processes, and quantities within a domain using SULO semantics.
    • Formalize domain concepts in OWL, translating SULO-based conceptual models into logical axioms.
    • Re-design the pizza ontology by applying SULO patterns in OWL for the pizza domain and validate them using tooling.
  2. Publish and maintain ontology projects following FAIR practices
    • Initialize a new FAIR ontology project using OntoStart, including metadata files, documentation structure, and CI-based quality checks.
    • Use Protégé 7 to build, visualize, and test ontology class structures.
    • Use owlready2 8 in Python to construct, manipulate, and reason over ontologies programmatically.
    • interpret FOOPS! [2] FAIRness reports and identify steps to improve ontology FAIRness.

🕒 Schedule (May 10/11 2026)

A half-day tutorial (4h) with two core components

Activity Duration Description OWL constructs introduced  
Tutorial overview 5 min Introduction to the tutorial    
What is an ontology? 10 min Overview of what ontologies are, and how they differ from terminologies, vocabularies, and schemas    
SULO quick overview 10 min Brief overview of classes and relations, the SULO postcard as a reference material    
OWL Declarations 15 min Declaring classes and individuals within an OWL ontology, importing SULO Entity Declarations (Class, Object and Data Property, Individual), Axioms (Class, Subclass, Disjoint), Annotation Properties, OWL Imports  
Spatial objects & their composition 20 min Describing necessary and/or sufficient conditions for class membership, focusing on pizzas and their parts Class Expression Exioms: Class Expressions, Propositional Connectives, Existential and Universal Quantification, Object Property Cardinality Restrictions (minimum, maximum, exact), Object Subproperties ( sub, inverse, domain, range, Functional, Transitive), Complex role inclusions  
Qualities 15 min Qualities as intrinsic characteristics, focusing on the spicyness of a pizza and its ingredients DisjointUnion  
Quantities 15 min Quantities as associated features, focusing on a numeric representation of the spicyness quality of an ingredient Functional data property, Data Property Cardinality Restrictions, Datatype Restriction  
Processes, process parts, and roles 30 min Processes, their parts, participants and their roles, development (maintainance of identity) and transformation (entities are created and destroyed), focusing on making of the pizza dough and crust Individuals, Individual equality and inequality  
Information Entities 15 min The receipt obtained after having paid for the pizza    
break 15 min      
Time 15 min Time as a measured quantity that can be associated to processes or objects, and temporal ordering, focusing on when a pizza order was received, the pizza baked, and when it was delivered    
Spatial containment and movement 15 min Differentiating spatial containment from parthood, movement of objects within a process, focusing on the addition and removal of the pizza in the pizza delivery box    
Q&A 30 min Discussion of modeling approaches within and beyond the tutorial    
OntoStart Deployment 15 min Publishing FAIR ontologies with documentation through Github actions Ontology IRI, OWL Versioning, Ontology Annotations, OWL Syntaxes  
FAIRness assessment 10 min Examining the output of a FOOPS! fairness assessment report    
Wrap up 5 min Q&A and wrap up    

Speakers

Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University and co-founder of the Department of Advanced Computing Sciences. He is a leading researcher in biomedical ontologies, knowledge graphs, and Semantic Web technologies. He co-founded the FAIR principles, leads major EU and US research initiatives, and has extensive experience teaching ontology engineering, knowledge graphs, and Semantic Web technologies at undergruadate and graduate level. He is a co-creator of SULO and created the ontostart FAIR ontology template project.

Remzi Celebi is an Assistant Professor in the Department of Advanced Computing Sciences at Maastricht University. His research focuses on semantic data integration, biomedical ontologies, knowledge graphs, and machine learning methods for health applications. Remzi is an experienced instructor and teaches courses on semantic web, knowledge graphs, machine learning, and FAIR data stewardship. He regularly supervises MSc and PhD students in ontology engineering, data integration, and representation learning.