Q:
Generate an RDF rendition of the following using Turtle Notation, terms from Schema.org, and hash-based relative hyperlinks for subject and object denotation. Use a single code-block for response output.

Knowledge Graphs - Foundations and Applications

Prof. Dr. Harald Sack

## Introduction
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. Overall, the goal of this course is to provide a broad overview of knowledge graphs and their underlying technologies as well as their significance in today's digital world.

October 11, 2023 - November 21, 2023
Language: English
Big Data and AI, Expert

### Course information

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.

### What you'll learn

* Basic understanding of knowledge graphs
* Basic understanding of ontologies
* Basic understanding of Semantic Web Technologies

### Who this course is for

* students
* practitioners of computer science, digital humanities, and information sciences
* enthusiasts with an AI related background and an interest in symbolic knowledge representation