The information available today exceeds all limits. Access to this abundance of data is only possible via search engines and sophisticated information processing applications. To enable the transition from raw data to well-structured knowledge, technologies such as natural language processing, information retrieval, and data and knowledge mining must be applied. In this MOOC, you will learn the fundamentals of natural language processing as well as the basics of Linked Data-based knowledge representation and machine learning to enable the transition from unstructured data to machine processable knowledge.
Self-paced
Language: English

Course information


The information available today exceeds all limits. Access to this abundance of data is only possible via search engines and sophisticated information processing applications. To enable the transition from raw data to well-structured knowledge, technologies such as natural language processing, information retrieval, data and knowledge mining must be applied. In the course of this transition, unstructured data such as natural language text, is analyzed based on statistical language models and machine learning, to represent the contained information with the help of formal knowledge representations. In general, “Information Service Engineering” covers the conceptualization, development, and maintenance of long-term operation of services for the exploitation, processing, and dissemination of information. In this lecture, students will learn the fundamentals of natural language processing, knowledge mining, machine learning, linked data engineering, as well as information retrieval required for the development of information services.

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General Course Information:

  • Course language: English
  • Weekly workload: 3 - 4 hours (Depending on your level of knowledge, this time may vary)
  • Course duration: 6 weeks from April 16 to May 28, 2018
  • Course Participants have two weeks after the course is completed to submit the Final Exam
  • Deadline for the Final Exam: June 11, 2018

Requirements for this course:

  • a basic understanding of web technologies, such as URL and HTTP
  • a basic understanding of mathematics, esp. statistics and probability theory
  • a basic knowledge of database technology such as relational databases and SQL query language

Intended Audience

  • students of computer science or related subjects on the bachelor or master level
  • researchers and scientists interested in natural language processing, linked data engineering, and machine learning
  • young professionals, esp. knowledge engineers, data & web scientists

Course contents


  • Week 1:

    Natural Language Processing I
  • Week 2:

    Natural Language Processing II
  • Week 3:

    Linked Data Engineering I
  • Week 4:

    Linked Data Engineering II
  • Week 5:

    Machine Learning Basics
  • Week 6:

    Putting it all together
  • Final Examination

Enroll me for this course


If you would like to enroll for this course, there are no formal prerequisites or limitations. The course is free and open for everyone. Just register for an account on openHPI and go for the course!

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This course was held from Apr 16, 2018 through Jun 11, 2018.

5700 learners enrolled.

Certificate Requirements


  • Gain a qualified certificate by booking it and gaining a record of achievement. Find out more in the qualified certificate guidelines.
  • Gain a record of achievement by earning more than 50% of the maximum number of points from all graded assignments.
  • Gain a confirmation of participation by completing at least 50% of the course material.

Find out more in the certificate guidelines.

This course is offered by


Prof. Dr. Harald Sack

Harald Sack is Professor for Information Services Engineering at FIZ Karlsruhe - Leibniz Institute for Information Infrastructure and Karlsruhe Institute of Technology. After graduating in computer science at the University of the Federal Forces Munich Campus in 1990, he worked as systems/network engineer and project manager in the signal intelligence corps of the German federal forces from 1990–1997. In 1997 he became an associated member of the graduate program ‘mathematical optimization’ at the University of Trier, where he obtained a PhD in Computer Science in 2002. From 2002–2009 worked as PostDoc at the Friedrich-Schiller-University in Jena. From 2009 - 2016 he worked as Senior Researcher and head of the research group 'semantic technologies’ at the Hasso Plattner-Institute for IT-Systems Engineering (HPI) at the University of Potsdam.

His areas of research include semantic web technologies, knowledge representations and ontological engineering, multimedia analysis & retrieval, machine learning and semantic enabled retrieval.

He is charter member and general secretary of the 2008 founded German IPv6 Council. He has served as Senior PC member or PC member of numerous international conferences and workshops related to semantic technologies as well as program chair, scientific chair or general chair.

Harald Sack has published more than 130 papers in international journals and conferences including three standard textbooks on networking technologies. He is co-founder of yovisto GmbH (www.yovisto.com).

Dr. Maria Koutraki

Maria Koutraki is a Postdoctoral researcher at FIZ Karlsruhe, Leibniz Institute for Information Infrastructure as well as at the Institute of Applied Computer Science and Formal Representations (AIFB) at Karlsruhe Institute of Technology (KIT). After graduating from the Computer Science department in University of Crete (Greece) in 2009, she continued her master studies in the Foundation of Research and Technology Hellas (FORTH) being part of the Information Systems group. In 2012, she started her PhD at the University of Paris-Saclay. The topic of her thesis was “Approaches Towards Unified Models for Integrating Web Knowledge Bases”. She obtained her PhD from the same University in 2016.
Her research interests include, semantic web technologies, knowledge graphs, knowledge representation, data mining and machine learning.
She has published her scientific contributions in top-ranked conferences like CIKM, ISWC, ESWC, EDBT etc.

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