Semantic Web and Linked Data

The course is not on the list Without time-table
Code Completion Credits Range Language
MIE-SWE.16 Z,ZK 4 2P+1C
Department of Software Engineering

Students learn modern standards, techniques, tools, methodologies and vocabularies used for knowledge representation according to the Linked Data principles, which are a modern take on the semantic web. The course covers practical aspect such as data extraction, triplification (conversion to RDF), linking and publishing. Students will be familiarized with widely used vocabularies and ontologies such as RDF, RDFS, OWL, FOAF, SKOS, Dublin Core etc.


Basics understanding of the following techniques:

- relational databases (SQL)


- object-oriented programming (Java preferred, C#)


Syllabus of lectures:

1. Linked Data and the Semantic web - motivation, examples

2. RDF data model, RDF Schema, common RDF serializations (Turtle, RDF/XML)

3. Commonly used vocabularies: RDF, RDFS, SKOS, Dublin Core, VoID, FOAF, VCard

4. Publishing of RDF and Linked Data, RDFa, RDFa Lite, Microformats, Microdata

5. RDF and Linked Data Storage - Triple (Quad) Stores: Openlink Virtuoso, Apache Jena Fuseki, OpenRDF Sesame

6. Networking among students for link discovery, early student project results

7. SPARQL query language

8. Linking of Linked Data: Principles and tools (Silk, Limes)

9. Typical process of triplification, examples

10. OWL - Web Ontology Language

11. Visualizing Linked Data

12. More vocabularies: GoodRelations

13. Conclusions

Syllabus of tutorials:

1. Linked Data examples, manual description of resources in RDF

2. Identification of suitable data sources and possible linking

3. Triplification of selected data sources - vocabularies, URIs

4. Data storage and querying using SPARQL

5. Data linking

6. Using Linked Data in applications

7. Presentation

Study Objective:

At the end of this course, students should be familiar with the Linked Data principles and the semantic web and should be able not only to consume Linked Data in their applications, but also to contribute to the Linked Data web by publishing their own datasets according to commonly used techniques and vocabularies.

Study materials:

1. Textbook: Tom Heath, Christian Bizer: Linked Data: Evolving the Web into a Global Data Space. http://linkeddatabook.com/

2. Christian Bizer, Tom Heath, Tim Berners-Lee: Linked Data - The Story So Far http://tomheath.com/papers/bizer-heath-berners-lee-ijswis-linked-data.pdf

3. Example datasets: http://linked.opendata.cz

Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2019-10-18
For updated information see http://bilakniha.cvut.cz/en/predmet4654006.html