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Informatik 5
Information Systems
Prof. Dr. M. Jarke
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Knowledge Graphs Praktikum

Type Weekly Practical course (basic level)
Term SS 18
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In this course we will give a basic practical introduction to working with knowledge graphs.

Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). The 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. There are many examples. 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)

Course dates

Meeting typeDate/TimeRoomStarting on
Weekly meeting Thursday, 00:00 - 00:00 12. April 2018

Prerequisites

We expects 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.

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