- Course type
- Winter Term 2021/2022
moodle course available
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.
In this seminar, students will learn about state-of-the-art KG technologies and investigate relevant research problems in the field, including:
- Creating KGs from (semi-)structured or 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
- Benchmarking KG technologies
The seminar includes four mandatory sessions:
- Kick-off session (start of the semester): Lecture on the foundational technologies of the seminar and presentation on the list of topics.
- Preliminary presentation (start of the semester): Seminar participants present initial ideas of the seminar thesis.
- Intermediate presentation (mid-semester): Seminar participants report on the progress of their theses.
- 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).
Basic knowledge about knowledge graphs, databases, or semantic web is highly recommended but not mandatory.