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Knowledge Graphs

Micro-credential

February – June 2024

Managing data on one machine for one specific kind of use is fairly straightforward. It is from the moment that that initial dataset needs to be shared with more than one application and needs to be combined with other datasets managed by other organizations on different machines, that more complex computer science and information technology problems arise. In this course we will deep-dive in the current state of the art in creating Knowledge on WebScale. Your personal data, data published publicly on the Web and data explicitly shared with you, becomes your Knowledge Graph that applications and services can use to assist you in your day to day activities.

Data scientists and engineers today claim 80% of their time goes to preparing and integrating the data: let us take you on a quest to fully automate data integration.



Several vacancies in Flanders require knowledge of the Flemish data space in order to share data interoperable. In this series of lessons, the professional learns to prioritize what is important when publishing data, and how he can contribute to a future in which data integration and negotiations over access to data can be fully automated.

A basic knowledge of JavaScript, HTTP, and command line is required

The language of instruction is English, which requires a sufficient command of the English language.



Micro-credentials are small courses of academic level that focus on specific competencies. They often consist of one or several subjects which are also taught in an university bachelor's or master's degree.

If you pass the micro-credential, you will receive a certificate as proof that you have completed the acquired competencies. So you also acquire real official credits who are recognized in your further career, also internationally. They can also lead to exemptions for other courses, also at other institutions and organizations.

You will receive a certificate of the micro-credential + credit certificate when you pass the corresponding exam (3 ECTS points).



Lecturer-in-charge

Prof. dr. ir. Pieter Colpaert, Department of Electronics and Information Systems, Ghent University

Co-lecturer

Prof. dr. ir. Ruben Verborgh, Department of Electronics and Information Systems, Ghent University


Contents

Open Data

  • Web Scraping
  • Legal aspects of data reuse
  • Findable, Accessible, Interoperable and Reusable data
  • Open Data portals

The quest for the universal data model

  • Knowledge Representations: key–val, resource-based, triple-based
  • Linked Data and the RDF data model
  • Linked Data and its serializations
  • Property graphs and RDF*
  • Logic with N3

Data Architectures

  • Linked Data Fragments
  • Event sourcing and Linked Data Event Streams
  • RDF Stream Processing
  • The Open World Assumption
  • Conway’s law

Web Querying

  • An introduction to SPARQL
  • Querying endpoints
  • Link Traversal
  • Hypermedia-based querying
  • Data summaries

Building Linked Data spaces

  • Data Spaces with IDSA
  • Metadata management with DCAT
  • Identity management with Solid-OIDC
  • Authorization and policies with WAC, ACP, ODRL and N3 rules
  • Personal data management with Solid
  • Cross-app interoperability with Solid
  • Data provenance with PROV-O, P-Plan, SDS
  • Ontology engineering with SKOS, RDFS and OWL
  • Validating RDF and building application profiles with SHACL and ShEx

Guest Lectures from European data tech companies and data publishers

Competences

Initial competences

  • Being able to read HTTP messages (URL, method, body, response codes, headers…)
  • Executing HTTP requests via the browser and the command-line
  • Reading and writing data from/in a CSV-file, a JSON-file and relational databases
  • Making small JavaScript programs in the browser and Node.js (reading files, performing HTTP interactions)

Final competences

  • Arguing the positioning, importance, and limitations of open data
  • Choosing the appropriate Web API to publish knowledge graphs
  • Modeling data as RDF graphs
  • Publishing knowledge graph on the Web from raw data
  • Designing a data architecture with fully automated data adoption and assessing trade-offs
  • Building a Linked Data vocabulary and application profile in RDF
  • Interpreting and creating SKOS, RDFS, and OWL constraints
  • Interpreting provenance of RDF data
  • Performing validation on RDF data
  • Querying the Web of Linked Data using Comunica
  • Positioning the industry opportunities and challenges on graph data



Practical info


Fee

322,60 euro

Course material is not included.

SME portfolio

Ghent University accepts payments via the SME portfolio (www.kmo-portfolio.be; use authorization code DV.O103193).



You can no longer register for this course.



  • The lessons start from February 15, 2024 and always take place on Thursdays from 10 am to 1 pm, in room 0.2, building 125 at the Technology Park in Zwijnaarde (note: paid parking!)
  • Participants take the classes together with students of the Master of Science in Bioinformatics and of the Master of Science in Computer Science Engineering.
    You can download the schedule of classes here.
  • A personal laptop is required
  • The training is supported by the Ufora learning platform, which contains, among other things, the course material.
  • Exam: oral exam with written preparation, open book

Organisation

Universiteit Gent
UGent Academie voor Ingenieurs
Secretariaat
 Els Van Lierde
Technologiepark 60
9052 Zwijnaarde
Tel.: +32 9 264 55 82
ugain@UGent.be