Validating semantic knowledge graphs using SHACL | Veronika Heimsbakk | Knowledge Connexions 2020
Автор: Connected Data
Загружено: 2025-03-13
Просмотров: 190
Prior to the Shape Constraint Language (SHACL) we had no W3C standardization for validation our semantic knowledge graphs against a set of constraints.
By constraining RDF using SHACL we gain the possibility of exactly this—validating semantic knowledge graphs under a closed world assumption!
This masterclass will introduce you to the SHACL Core constraints, demonstrate how to constrain your data and what happens if the data conforms false (or true for that matter!).
Goals:
Learn about the SHACL Core constraints
Gain hands-on experience for constraining your RDF data using SHACL and identifying errors in validation reports
Key Topics:
Terminology and concepts of semantic knowledge graphs
Comparing SHACL and the Web Ontology Language (OWL)
Introduction to SHACL
Hands-on:
Building shapes for ABoxes
Validation and validation errors
Target Audience:
Information architects
Content creators
Data modellers
People interested in high data integrity
Level:
Beginner/Intermediate (depending on your prior knowledge of SHACL)
Prerequisites:
Some knowledge and/or experience with RDF is needed.
Format:
The first part includes a traditional lecture
Followed by a hands-on session that participants can follow in their editor of choice
Outline:
Lecture including
Basis terminology, concepts and history
Introduction to SHACL Core constraints
Hands-on demonstration
A brief look at some data examples
Building constraints from scratch, using SHACL Core
Using validation engine (SHACL playground and through a Java framework)
Summary
Discussion, Q&A, resources
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Veronika Heimsbakk: Senior Consultant, AI & Data Science, Capgemini
Senior consultant and semantics nerd at Capgemini I&D Norway. After receiving her degree from the University of Oslo she has been working with semantic technologies as a developer, information architect and advisor for clients in both public and private sector, nationally and continental. Since 2016 she has worked a great deal with the Shape Constraint Language; developing engines, constraining data for validation and for modeling knowledge. Apart from enjoying the wonders of semantic knowledge graphs, she loves teaching kids to code, LaTeX and hiking.
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