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

课程信息

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
Visit us on Facebook: https://www.facebook.com/OpenHPI

You can find more video lectures at www.tele-task.de.

课程内容

  • 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

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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. 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.