A very warm welcome to "Knowledge Graphs"! You can find the "static" information for the course on this page. The TISS page contains further information, also for registering.
Almost every one of us has come into contact with a Knowledge Graph (KG): If we perform a web search, such as the Google search shown in the figure below, we do not only get a list of websites (shown on the left of the figure), but also a panel (highlighted with a yellow border on the right) with information coming from Google's Knowledge Graph.
Beyond Google, the concept of Knowledge Graphs has become - depending on who you ask - the next step in the evolution of how we represent and reason about knowledge, or a shift in how we utilize knowledge in Artificial Intelligence. The answer to the question "What is a Knowledge Graph?" and what we can do with it is of course what this course is all about, but if you are interested to read more about current research on the topic, you can follow the link below.
The aim of this is course is to gain a deep understanding of Knowledge Graphs divided into three blocks. An overarching aim of the course is to understand the connections between Knowledge Graphs, Artificial Intelligence, Machine Learning and Data Science.
Widely-applied, large family of ML models.
Highly expressive, diverse family of logical models.
Using the KG structure as a neural network.
Overview of data models in different communities.
The big picture of building IT architectures for KGs.
Making use of the knowledge in the KG.
How to create a KG from heterogeneous data?
How to update, correct and complete a KG?
Overview of diverse applications.
Concrete applications in finance and economics.
Which service to provide based on KGs?
... between KGs, AI, ML and Data Science.
All topics will be covered by lectures offered via distance learning. Assessment is by a mini-project. The procedure is in three simple steps:
This will be supported by discussions and feedback throughout the course.
For more details on the organization of the course, including examples for mini-project ideas, exact ECTS distribution, curriculum assignment, etc., please see the below link:
Email the lecturer Prof. Dr. Emanuel Sallinger (sallinger@dbai.tuwien.ac.at) and the KG Lab team!
The Knowledge Graph Lab at TU Wien is part of the Database and Artificial Intelligence (DBAI) group at the Institute of Logic and Computation of the Faculty of Informatics. It is funded by the Vienna Science and Technology Fund (WWTF) under the Vienna Research Group scheme - "Vienna Research Group on Scalable Reasoning in Knowledge Graphs" (VRG18-013).
We would like to thank DeepReason.ai, a spin-out of the University of Oxford, for the joint development of the Vadalog Knowledge Graph Management System and the collaboration.
For inquiries please contact Prof. Dr. Emanuel Sallinger, head of the Knowledge Graph Lab at TU Wien, at <sallinger@dbai.tuwien.ac.at>.