This is a master level lab course. Strongly motivated bachelor students can contact the course coordinator directly; these applications will be considered if there is space available.

Introduction

Knowledge Graphs are large graphs used to capture information about the real world in such a way that is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). These graph also contains all sorts of information about these entities (e.g., age, opening hours, …) and relations between them (e.g., this shop is located in Aachen).

Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, like Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answer systems like IBM Watson, make use of knowledge graphs to provide information to their users. Besides these, also other information graphs, are in use by large organizations to improve or personalize their services. Examples include the Facebook graph, the Amazon product graph, and the Thompson Reuters Knowledge Graph.

In this course we will give a basic practical introduction to working with these graphs. As this is the first time this practical course is thought, some parts are still under construction. Currently, we plan to cover the following in the course:

  • Graph representation of data
  • Use of vocabularies and ontologies
  • Searching information in knowledge graphs
  • Information extraction
  • Data mining techniques for knowledge graphs
  • Knowledge graph completion (predicting links, finding anomalies)

Preparing for the course

One options to prepare yourself for the course is watching trough these videos by Harald Sack: https://open.hpi.de/courses/semweb2015 and this coursera MOOC: https://www.coursera.org/learn/web-data/

Preliminary schedule

TBA

Required Prior knowledge

We expect that you are able to program and use data structures and algorithms. Having experience with graphs is not required, but is definitely useful. We will also have some data mining related task, so past experience in that domain is also an asset.

Note that the language of this course is English.

The course is organized with the appreciated support from OSTHUS