TH Köln

Master Digital Sciences

Dokumente zur Akkreditierung des Studiengangs

Modul »Linked-Open Data and Knowledge Graphs« (LOD)

Informationen zur Organisation des Moduls

Modulverantwortung
Prof. Dr. Konrad Förstner (Fakultät F03)
Sprache
Englisch
Angeboten im
Wintersemester (Dauer 1 Semester)
Ort
Campus Köln Süd, oder remote
Anzahl Teilnehmer*innen
minimal 6, maximal 20
Vorbedingung
keine
Empfehlung
Basic Python programming skills
ECTS
6
Aufwand
Gesamtaufwand 180h
Kontaktzeit
60h (30h Vorlesung / 15h Übung / 15h Projektbetreuung)
Selbstlernzeit
120h (davon 120h eigenständige Projektarbeit)
Prüfung
Semesterbegleitendes Projekt
Vermittelte Kompetenzen
Implement Concepts, Deploy Products, Optimize Systems, Apply Standardization
Beziehung zu globalen Studiengangskriterien
Interdisziplinarität, Digitalisierung

Beitrag zu Handlungsfeldern

Nachfolgend ist die Zuordnung des Moduls zu den Handlungsfeldern des Studiengangs aufgeführt, und zwar als anteiliger Beitrag (als ECTS und inhaltlich). Dies gibt auch Auskunft über die Verwendbarkeit des Moduls in anderen Studiengängen und über die Beziehung zu anderen Modulen im selben Studiengang.

Handlungsfeld ECTS (anteilig) Modulbeitrag zum Handlungsfeld
Generating and Accessing Knowledge 4

The course introduces the basic concepts of Linked Open Data (LOD) as well as the construction of knowledge graphs.

Acting Responsibly 1

The course introduces the basic concepts of Linked Open Data (LOD) as well as the construction of knowledge graphs.

Designing Innovations and Products 1

The course introduces the basic concepts of Linked Open Data (LOD) as well as the construction of knowledge graphs.

Learning Outcome

The course aims to equip students with the knowledge and skills needed to leverage Linked Open Data (LOD) and Knowledge Graphs effectively in various professional contexts. Through active participation, discussions, and a hands-on project, students will gain a deep understanding of how LOD and knowledge graphs are transforming the way data is managed and knowledge is represented in the digital age. We will explore real world applications like Wikidata and Open Research Knowledge Graph (ORKG).

After finishing this course students are able to use, extend and construct knowledge graphs.

Inhaltliche Beschreibung des Moduls

  1. Linked Open Data
  2. Knowledge Graphs
  3. Semantic Searches
  4. RDF
  5. SPARQL
  6. Applications - Wikidata and ORKG

Lehr- und Lernformen

The course adopts an interactive seminaristic style, fostering active engagement and collaborative learning among participants. In addition to comprehensive lectures, the seminar incorporates paper discussions, enabling students to critically analyze and debate research papers and case studies related to Linked Open Data and Knowledge Graphs. Furthermore, students will have the opportunity to showcase their understanding through presentations, where they can articulate their insights and findings on relevant topics. To reinforce practical application, the seminar culminates in a programming project, where participants will design, implement, and evaluate a real-world project that involves the construction and querying of a knowledge graph, applying the principles learned throughout the seminar. This multifaceted approach ensures that students not only acquire theoretical knowledge but also gain hands-on experience and the ability to apply these concepts in practical scenarios.

Zur Verfügung gestelltes Lehrmaterial

  • lecture slides and videos
  • exercises

Weiterführende Literatur

  • Hitzler, Pascal. “A review of the semantic web field.” Communications of the ACM 64.2 (2021): 76-83. https://doi.org/10.1145/3397512

  • “Knowledge Graphs – Methodology, Tools and Selected Use Cases”, Fensel, D., Şimşek, U., Angele, K., Huaman, E., Kärle, E., Panasiuk, O., Toma, I., Umbrich, J., Wahler, A., 2020, ISBN 978-3-030-37439-6