course: Seminar on Knowledge Graphs

number:
150542
teaching methods:
seminar
media:
Moodle
responsible person:
Jun. Prof. Dr.-Ing. Maribel Acosta Deibe
lecturer:
Jun. Prof. Dr.-Ing. Maribel Acosta Deibe (Mathematik)
language:
english
HWS:
2
CP:
4
offered in:
winter term

dates in winter term

  • start: according to agreement

goals

The seminar includes four mandatory sessions:

  1. Kick-off session (start of the semester): Lecture on the foundational technologies of the seminar and presentation on the list of topics.
  2. Preliminary presentation (start of the semester): Seminar participants present initial ideas of the seminar thesis.
  3. Intermediate presentation (mid-semester): Seminar participants report on the progress of their theses.
  4. Final presentation (end of the semester): Seminar participants present their theses and final results.

In addition to the mandatory appointments, seminar participants may schedule individual meetings with the professor to discuss the progress of the work (highly recommended).

content

Knowledge Graphs (KG) allow for representing inter-connected facts or statements annotated with semantics. In KGs, concepts and entities are typically modeled as nodes while their connections are modeled as directed and labeled edges, creating a graph.

In recent years, KGs have become core components of modern data ecosystems. KGs, as building blocks of many Artificial Intelligence approaches, allow for harnessing and uncovering patterns from the data. Currently, KGs are used in the data-driven business processes of multinational companies like Google, Microsoft, IBM, eBay, and Facebook. Furthermore, thousands of KGs are openly available on the web following the Linked Data principles (https://lod-cloud.net/).

In this seminar, students will learn about state-of-the-art KG technologies and investigate relevant research problems in that field, including:

  • Creating KGs from (semi-)structured on unstructured sources
  • Representing facts in KGs: RDF, RDFS, OWL, Property Graphs
  • Querying KGs: SPARQL, CypherQL
  • KG Quality: metrics and tasks to enhance the quality of KGs
  • Vector representations for KGs
  • Publication of KGs on the web

requirements

Basic knowledge about databases or semantic web is highly recommended but not mandatory.

miscellaneous

Deadline 30.10.2020: maribel.acosta@rub.de