Study and teaching topics of the department Data Science and Digital Libraries

The main areas of study of the department include semantic technologies, knowledge engineering, data integration, knowledge representation. Besides theoretical basics, the application of concrete tools is practiced in the laboratories and the relation to industrial practice is established through participation in projects.  

Courses offered by the department include in particular:

  • Lecture Knowledge Engineering and Semantic Web - teaches the basics of knowledge representation and semantic technologies.
  • Seminar and Proseminar Data Science and Digital Libraries - enables students to deepen their knowledge in the area of Data Science and Digital Libraries in specific topics
  • Theses (Bachelor/Master) - enables students to gain comprehensive scientific and technical knowledge and skills in a specific application project 

Theses (Bachelor/Master)

We are happy to supervise committed students in the context of final theses. Ideally, interested candidates have already attended a lecture and/or seminar at our research group. If you are interested, please send an email with a short CV, your technology interests and grades to one of the following supervisors:

  • Dr. Javad Chamanara: topics- Machine Learning and Semantic Technologies; Technologien -  Java, Python, RDF, SQL, SPARQL, InfluxDB
  • Dr. Markus Stocker: topics- Wissensgraphen, Open Research Knowledge Graph, Forschungsinfrastrukturen; Technologien - Java/Kotlin, Python, Neo4J, Spring, ReactJS, RDF/OWL, SPARQL
  • Dr. Gábor Kismihók: topics - Open Educational Resources and Skill Matching; Technologies - Data Science, ReactJS 
  • Dr. Oliver Koepler: topics - Research Daten Management; Technologies - RDF/OWL, CKAN, Python, Semantic MediaWiki
  • Dr. Jennifer D'Souza: topics- Knowledge Extraction; Technologies - RDF/OWL, Natural Language Processing
  • Dr. Oliver Karras: topics- Software Engineering; Research Knowledge and Data Engineering

All information about offered courses can be found in the course catalog of the university and in the modul catalog of the faculty. If you are interested in writing a thesis, we recommend that to attend the courses (lecture and seminar) offered by the department.