Knowledge Graphs

Lecturers

Details

Course type
Lectures
Term
Summer Term 2021
E-Learning
moodle course available

Description

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 GoogleMicrosoft, IBM, eBay, and Facebook. Furthermore, thousands of KGs are openly available on the web following the Linked Data principles.

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 to show how KG technologies work in practice.

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. Property Graphs
  10. Knowledge Graph Applications

After successful completion of this course, students will be able to:

  • understand the formalisms for modelling, querying, and reasoning over knowledge graphs,
  • apply the aforementioned formalisms to execute operations over knowledge graphs,
  • create prototypes of queryable knowlege graphs using the techniques learned during the course,
  • understand the role of knowledge graphs as a foundation to solve other problems in Artificial Intelligence,
  • remember examples of real-world knowledge graphs and their applications in industry,
  • communicate about the above aspects in English.
Lecture Organization

The lecture is held as a 4 SWS course:

  • Lecture sessions (2 SWS): Mondays 12.00-14.00
  • Exercises sessions (2 SWS): Thursdays 12.00-14.00
Prerequisites

Basic knowledge about the following topics is highly recommended but not mandatory:

  • Graph theory
  • Set theory
  • Databases
  • Logic
Literature
  • Aidan Hogan et al. Knowledge Graphs. 2020. (Sections 1 and 2). 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 & Hall/CRC, 2009.

Exercises on Knowledge Graphs (Übungen)

The exercises will start one week after the lecture.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
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