An error occurred while loading the video player, or it takes a long time to initialize. You can try clearing your browser cache. Please try again later and contact the helpdesk if the problem persists.
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.
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.
Join openHPI's official Twitter Feed: @openHPI
General Course Information:
Requirements for this course:
This course was rated with 4.71 stars in average from 42 votes.
Find out more in the certificate guidelines.
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 technologies, knowledge graphs and knowledge representations, ontological engineering, knowledge extraction, machine learning, semantic & explorative search.
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 200 papers in international journals and conferences including three standard textbooks on networking technologies. He is co-founder of yovisto GmbH (www.yovisto.com).
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.