Knowledge Graphs

Even though it affects our lives every single day, most of us have no idea what a knowledge graph is. Asking Alexa about the weather tomorrow or searching for the latest news on climate change via Google, knowledge graphs constitute the backbone of today’s state-of-the-art information systems. From improving search results over question answering and recommender systems up to explainable AI systems, the applications of knowledge graphs are manyfold.

自十二月 15, 2020起开始自学
语言: English
Big Data and AI, Expert


In this course you will learn what is necessary to design, implement, and use knowledge graphs. The focus of this course will be on basic semantic technologies including the principles of knowledge representation and symbolic AI. This includes information encoding via RDF triples, knowledge representation via ontologies with OWL, efficiently querying knowledge graphs via SPARQL, latent representation of knowledge in vector space, as well as knowledge graph applications in innovative information systems, as e.g., semantic and exploratory search.

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 October 27 to December 8, 2020
  • Deadline for the Final Exam: December 14, 2020 (11:55pm UTC)

Requirements for this Course:

  • Basic understanding of web technologies, such as URL and HTTP
  • Basic understanding of mathematics, in particular statistics and probability theory
  • Basic knowledge of database technology, such as relational databases and SQL query language

Intended Audience:

  • Students of computer science or related subjects at bachelor or master level
  • Researchers and scientists interested in the web, knowledge representation, semantic web technologies, ontology engineering, machine learning, artificial intelligence
  • Young professionals, in particular knowledge engineers, data & web scientists
  • Students, researchers and professionals in the field of digital humanities and cultural heritage (e.g. working in archives, libraries, and museums)

Teaching Team

The course is run by the research group Information Service Engineering of FIZ Karlsruhe and Karlsruhe Institute of Technology (AIFB).

Social Media

Follow FIZ ISE on Twitter @fiziseka
Follow openHPI on Twitter: @openHPI
For tweets about this course please use the hashtag #knowledgegraphs2020
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You can find more video lectures at


  • Week 1:

    Knowledge Graphs in the Web of Data
  • Week 2:

    Basic Semantic Technologies
  • Week 3:

    Querying RDF with SPARQL
  • Week 4:

    Knowledge Representation with Ontologies
  • Week 5:

    Knowledge Graph Applications
  • Week 6:

    Advanced Knowledge Graph Applications
  • Final Exam


您可以访问所有评分测试并在最后获得课程证书,通过 重启课程. 页面了解更多


该课程是免费的。 只需在openHPI上注册一个帐户并参加课程!


Course End
12月 15 2020
Course Start
10月 27 2020




  • 合格证书授予那些进行了预定并且取得了达标成绩的用户 更多信息请访问 合格证书指南.
  • 课程证书 授予者需要至少取得课程总分的百分之 50%
  • 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%



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。



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(的共同创始人。


Dr. Mehwish Alam

Dr. Mehwish Alam is a Post-Doctoral Researcher/Senior Researcher at FIZ Karlsruhe - Leibniz Institute for Information Infrastructure and Karlsruhe Institute of Technology (KIT), Institute of Applied Informatics and Formal Description Methods (AIFB) in Information Service Engineering team. Before that she has conducted her Post-Doctoral Research at Laboratoire d'Informatique de Paris-Nord (LIPN), Paris, France (2016-2017) and Consiglio Nazionale delle Ricerche (CNR), Rome, Italy (2017-2019). The focus of her research is the development/application of Machine & Deep Learning techniques for Knowledge Graphs and Natural Language Processing.