Despite the fact that it affects our lives on a daily basis, most of us are unfamiliar with the concept of a knowledge graph. When we ask Alexa about tomorrow's weather or use Google to look up the latest news on climate change, knowledge graphs serve as the foundation of today's cutting-edge information systems. In addition, knowledge graphs have the potential to elucidate, assess, and substantiate information produced by Deep Learning models, such as Chat-GPT and other large language models. Knowledge graphs have a wide range of applications, including improving search results, answering questions, providing recommendations, and developing explainable AI systems. In essence, the purpose of this course is to provide a comprehensive overview of knowledge graphs, their underlying technologies, and their significance in today's digital world.
Мова: English
Big Data and AI, Expert
Інформація про курс
A knowledge graph is a structured representation of knowledge that is used to provide a comprehensive and interconnected view of a specific domain. In this course we will discuss the key features and characteristics of knowledge graphs. You will learn all what is necessary to design, implement, and apply knowledge graphs. The focus of this course will be on basic semantic technologies including the underlying 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 spaces, as well as knowledge graph applications in innovative information systems, as e.g., semantic and exploratory search. Furthermore the role of knowledge graphs in artificial intelligence and machine learning will be discussed, as well as their potential to improve explainability and trustworthiness of "black box" deep learning models such as Chat-GPT.
Чого ви навчитеся
- Basic understanding of knowledge graphs
- Basic understanding of ontologies
- Basic understanding of Semantic Web Technologies
- Basic understanding of ontology design and knowledge graph construction
- Basic understanding of knowledge graph embeddings
Для кого цей курс
- students
- practitioners of computer science, digital humanities, and information sciences
- enthusiats with an AI related background and an interest in symbolic knowledge representation
Зміст курсу
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
7 Final Exam
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Цей курс отримав у середньому 4.36 зірки від 106 голосів.
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Цей курс запропонований
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).