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Technische Universität München

Knowledge Graphs 

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 Google, Microsoft, 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.

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.
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