About this talk
An introduction to the practice of semantic data modeling, with focus on recognizing and avoiding bad practices when developing ontologies, taxonomies, knowledge graphs and other semantic data artifacts.
Based on the O'Reilly book "Semantic Modeling for Data"
<img class="b-lazy img-fluid" height="" src="http://sdmbook.panosalexopoulos.com/wp-content/uploads/2020/08/SMD_Cover.jpg" width="300">
Join his Masterclass for the chance to win a free copy!
- What are some basic semantic modeling elements and how they are implemented in OWL and SKOS
- Ambiguity, vagueness and other semantic phenomena that play a role in semantic modeling
- How bad descriptions of semantic modeling elements can compromise the human-interpretability of a semantic model and what to do about it
- How the erroneous usage of semantic modeling elements compromises the machine interpretability of a semantic model and what to do about it
- Knowledge engineers
- Data modelers who develop semantic data models
- Data scientists and machine learning engineers who consume and exploit knowledge graphs, ontologies, taxonomies and other semantic data models
- Understand important modeling pitfalls that undermine the quality and value of semantic models
- Develop concrete strategies and techniques for avoiding these pitfalls both when developing and using semantic models
We will consider OWL and SKOS as our modeling frameworks.
We will draw examples from well-known public semantic models such as Schema.org, KBPedia, FIBO and SNOMED
This class will be highly collaborative and interactive.
Beginner - Intermediate
Some knowledge of OWL and SKOS is desired but not necessary