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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.

自六月 11, 2018起开始自学
语言: English
Big Data and AI, Expert

课程信息

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

课程内容

  • 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

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该课程是免费的。 只需在openHPI上注册一个帐户并参加课程!
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Learners

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  • 参与证明 授予者需要至少学习了所有课程资料的百分之 50%

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该课程提供者

Prof. Dr. Harald Sack

哈拉尔德-萨克是卡尔斯鲁厄FIZ-莱布尼茨信息基础设施研究所和卡尔斯鲁厄技术研究所的信息服务工程教授。1990年在联邦部队慕尼黑校区大学计算机科学专业毕业后,1990-1997年他在德国联邦部队的信号情报团担任系统/网络工程师和项目经理。1997年,他成为特里尔大学 "数学优化 "研究生项目的联系成员,并于2002年获得计算机科学博士学位。2002-2009年在耶拿的弗里德里希-席勒大学担任博士后。2009-2016年,他在波茨坦大学哈索-普拉特纳信息技术-系统工程研究所(HPI)担任高级研究员和 "语义技术 "研究组组长。

他的研究领域包括语义技术、知识图谱和知识表示、本体工程、知识提取、机器学习、语义和探索性搜索。

他是2008年成立的德国IPv6委员会的特许成员和秘书长。他曾在许多与语义技术有关的国际会议和研讨会中担任高级计算机成员或计算机成员,并担任项目主席、科学主席或总主席。

Harald Sack在国际期刊和会议上发表了200多篇论文,包括三本网络技术的标准教科书。他是yovisto GmbH(www.yovisto.com)的共同创始人。

通过www.DeepL.com/Translator(免费版)翻译

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.