Hands-on experience of developing a semantic model in OWL and SKOS, with progressive exercises designed to illustrate pitfalls we need to avoid and dilemmas we need to break during the modeling process.
Based on the O'Reilly book "Semantic Modeling for Data"
Key Topics
* When to use SKOS versus when to use OWL
* When to model an entity as a class versus when to model it as an individual
* How to properly model subclasses, parts and other semantic relations
* How to choose meaningful names for the model’s elements
* How to document assumptions and design decisions
* How to deal with vagueness
Target Audience
* Knowledge engineers
* Ontologists
* Taxonomists
* Data practitioners who develop semantic data models
* Technical leaders who want hands-on semantic modeling experience
Goals
* Get hands-on experience using schemas, knowledge graphs and NLP to develop a long-tail SEO strategy
Session outline
* Hands-on experience with OWL, SKOS, and Protege
Format
* This class will be highly collaborative and interactive.
For the modeling exercises we will work on Protege (standalone and/or web version)
All the exercises and solutions will be made available to the attendees after the conclusion of the masterclass.
Level
* Beginner - Intermediate
Prerequisite Knowledge
* Basic knowledge of OWL and SKOS
* Attendance of Flawless Semantic Modeling in OWL and SKOS - The Theory is recommended but not obligatory
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Panos Alexopoulos: Head of Ontology, Textkernel
Working since 2006 at the intersection of data, semantics, and software, contributing to building intelligent systems that deliver value to business and society. Currently Head of Ontology at Textkernel BV, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain. Author of the book "Semantic Modeling for Data - Avoiding Pitfalls and Breaking Dilemmas" (O'Reilly, 2020)
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