Knowledge Graphs

NUMMER: 150336
KÜRZEL: KnowGr
MODULBEAUFTRAGTE:R: Jun. Prof. Dr.-Ing. Maribel Acosta Deibe
DOZENT:IN: Jun.-Prof. Maribel Acosta Deibe
FAKULTÄT: Fakultät für Informatik
SPRACHE: Englisch
SWS: 4 SWS
CREDITS: 5 CP
WORKLOAD: 150 h
ANGEBOTEN IM: jedes Sommersemester

BESTANDTEILE UND VERANSTALTUNGSART

a) Vorlesung Knowledge Graphs
(150336)
b) Übung (150337)

PRÜFUNGEN

FORM: schriftlich
ANMELDUNG:
DATUM: 0000-00-00
BEGINN: 00:00:00
DAUER:
RAUM:

LERNFORM

Vorlesung mit begleitenden Übungen

LERNZIELE

In this lecture, students will learn about the foundations of modelling, querying, publishing,
and reasoning over KGs. The topics will be complemented with exercises and Jupyter Notebooks
(https://jupyter.org/) to show how KG technologies work in practice.

INHALT

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 (https://lod-cloud.net/) principles. The
specific topics covered in the lecture are as follows:
1. Introduction to Knowledge Graphs
2. The Resource Description Framework (RDF)
3. RDF Schema (RDFS)
4. The SPARQL Query Language
5. Semantics of SPARQL
6. Linked Data: Knowledge Graphs and Ontologies on the Web
7. The Web Ontology Language (OWL)
8. Entailment Regimes
9. Reasoning over Knowledge Graphs
10. Property Graphs
11. Knowledge Graph Applications

VORAUSSETZUNGEN

Keine

VORAUSSETZUNGEN CREDITS

Bestandene Modulabschlussprüfung

EMPFOHLENE VORKENNTNISSE

Basic knowledge about the following topics is highly recommended
but not mandatory: Graph theory, set theory, databases, logic.

LITERATUR

∙ Aidan Hogan et al. Knowledge Graphs. 2020. https://arxiv.org/pdf/2003.02320.
pdf
∙ Andreas Harth. Introduction to Linked Data. (Specific chapters will be provided in the
lecture).
∙ Pascal Hitzler, Markus Krötzsch, Sebastian Rudolph. Foundations of Semantic Web
Technologies. Chapman and Hall/CRC, 2009.

AKTUELLE INFORMATIONEN

SONSTIGE INFORMATIONEN