OWL or SHACL – A Beginner’s Guide to Making the Right Choice
A Talk by Tara Raafat and Davide D'Amico
About this Talk
As organizations increasingly adopt knowledge graphs (KGs) to enhance their strategic objectives, one of the most pressing questions that arises is: should one utilize OWL or SHACL? Are both necessary, or can one substitute the other?
Understanding the differences, overlaps, and appropriate contexts for these technologies is crucial for the effective development of KGs. Whether KGs are intended for knowledge organization, search optimization, data interoperability, or serving as a foundation for large language models (LLMs), the choice between OWL and SHACL can influence the impact and scalability of the project, regardless of its size.
Making the right decision between OWL and SHACL is critical in ensuring that the ontology is not only functional but also maximally effective in addressing the problem at hand. However, these technologies are sometimes underutilized or not fully leveraged to their potential.
The aim of this masterclass is to introduce participants to the fundamentals of OWL and SHACL, their purposes, their strengths and their true usages through a hands-on exploration of a real-world domain example. Participants will engage with multiple problem statements and will be tasked with determining whether OWL, SHACL, or both should be applied to solve each issue, critically evaluating the implications of their decisions.
This masterclass is designed for individuals new to these technologies or those in the early stages of developing KGs within their organizations or projects. Attendees will gain a foundational understanding of OWL and SHACL, exploring their respective purposes, similarities, and distinctions.
A brief introduction to SPARQL will also be provided, enabling participants to test and validate their implementations. Through practical, hands-on exercises, participants will model ontologies and SHACL shapes using open-source tools, and apply queries to test their solutions.
By the end of the masterclass, participants will have a strong understanding of both technologies, and the ability to decide when and how to apply each effectively.
Key Topics
- OWL: Constructing ontologies using OWL
- SHACL: Creating SHACL shapes
- Decision-making: when to choose OWL or SHACL for solving specific problems
- SPARQL: Querying and validating implementations
Target Audience:
- Knowledge Engineers
- Data Engineers
- Data Analysts
- Managers overseeing the above roles
Workshop Goals
Participants will gain hands-on experience in creating KGs using OWL and SHACL, while developing an understanding of the purpose of each technology and the practical implications of selecting either for solving domain-specific problems.
Session Outline:
- Introduction to RDF, OWL, and open-source tools for implementation of ontologies
- Introduction to SHACL
- Quick Overview of SPARQL
- Practical problem statements to be solve by either constructing ontologies and/or SHACL shapes
- Running reasoners to validate ontology models
- Executing SPARQL queries to test against problem statements
Format:
This workshop will be highly interactive, emphasizing practical, hands-on learning. Participants will first be introduced to the foundational concepts of OWL and SHACL through introductory lectures.
These sessions will be followed by applied activities, where participants will address domain-specific problem statements utilizing these technologies.
Using Protégé, participants will develop ontologies and execute reasoning tasks.
Concurrently, they will employ an online playground to design and implement SHACL shapes.
Finally, participants will execute SPARQL queries within the Protégé environment to assess the efficacy of their solutions.
Level:
Beginner
Prerequisite Knowledge:
Familiarity with RDF is advantageous but not required.
Instructions:
- Download Protégé Desktop (NOT WebProtégé): https://protege.stanford.edu/
- Download ZIP / Clone GitHub repo: https://github.com/SemanticMasterclass/owl_shacl
About The Speakers
Head of Metadata Strategy, Bloomberg
Dr. Tara Raafat is the head of Bloomberg's metadata strategy at the CTO office. Tara is a semantic technologies specialist with 15+ years of experience in semantic knowledge modeling and knowledge driven applications in domains including but not limited to finance, healthcare, and insurance.
We’re a cloud tech company that provides organisations around the world with computing infrastructure and software to help them innovate, unlock efficiencies and become more effective. We also created the world’s first – and only – autonomous database to help organise and secure our customers’ data.
