Flawless Semantic Modeling in OWL and SKOS - The Theory

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"

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Key Topics

• 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

Target Audience

• Knowledge engineers
• Ontologists
• 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

Goals

• 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

Session outline

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

Format

This class will be highly collaborative and interactive.

Level

Beginner - Intermediate

Prerequisite Knowledge

Some knowledge of OWL and SKOS is desired but not necessary