Knowledge Graphs - Foundations and Applications (openHPI)

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

- enthusiats with an AI related background and an interest in symbolic knowledge representation