Knowledge Graphs
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.
Practical information:
Want to register?
- Register until: 11 Feb 2024
- Prerequisites: A basic knowledge of JavaScript, HTTP, and command line is required.
- Price: 322,60 euro
Georganiseerd door:
Content
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
Related courses
Summer School on Security and Privacy in the Age of AI
Summerschool - Heverlee - KU Leuven, UGent, VUB, Imec